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Common and Distinct Neural Patterns of Attention-Deficit/Hyperactivity Disorder and Borderline Personality Disorder: A Multimodal Functional and Structural Meta-analysis

  • Nanfang Pan
    Affiliations
    Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China

    Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China

    Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
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  • Song Wang
    Correspondence
    Song Wang, Ph.D.
    Affiliations
    Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China

    Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China

    Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
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  • Kun Qin
    Affiliations
    Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China

    Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China

    Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
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  • Lei Li
    Affiliations
    Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China

    Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China

    Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
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  • Ying Chen
    Affiliations
    Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China

    Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China

    Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
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  • Xun Zhang
    Affiliations
    Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China

    Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China

    Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
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  • Han Lai
    Affiliations
    Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China

    Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China

    Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
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  • Xueling Suo
    Affiliations
    Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China

    Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China

    Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
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  • Yajing Long
    Affiliations
    Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China

    Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China

    Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
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  • Yifan Yu
    Affiliations
    Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China

    Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China

    Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
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  • Shiyu Ji
    Affiliations
    Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China

    Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China

    Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
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  • Joaquim Radua
    Affiliations
    Imaging of Mood- and Anxiety-Related Disorders Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer, Centro de Investigación Biomédica en Red de Salud Mental, Barcelona, Spain

    Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom
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  • John A. Sweeney
    Affiliations
    Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China

    Department of Psychiatry, University of Cincinnati, Cincinnati, Ohio
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  • Qiyong Gong
    Correspondence
    Address correspondence to Qiyong Gong, M.D., Ph.D.
    Affiliations
    Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China

    Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, China
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Open AccessPublished:June 14, 2022DOI:https://doi.org/10.1016/j.bpsc.2022.06.003

      Abstract

      Background

      Attention-deficit/hyperactivity disorder (ADHD) and borderline personality disorder (BPD) have partially overlapping symptom profiles and are highly comorbid in adults. However, whether the behavioral similarities correspond to shared neurobiological substrates is not known.

      Methods

      An overlapping meta-analysis of 58 ADHD and 66 BPD whole-brain articles incorporating observations from 3401 adult patients and 3238 healthy participants was performed by seed-based d mapping. Brain maps were subjected to meta-analytic connectivity modeling and data-driven functional decoding analyses to identify associated neural circuit alterations and relations to behavioral dimensions.

      Results

      Both groups exhibited hypoactivated abnormalities in the left inferior parietal lobule, and altered clusters of the bilateral superior temporal gyrus were disjunctive in ADHD and BPD. No overlapping structural abnormalities were found. Multimodal alterations of ADHD were located in the right putamen and of BPD in the left orbitofrontal cortex.

      Conclusions

      The transdiagnostic neural bases of ADHD and BPD in temporoparietal circuitry may underlie overlapping problems of behavioral control, while disorder-specific substrates in frontostriatal circuitry may account for their distinguishing features in motor and emotion domains, respectively.

      Keywords

      Attention-deficit/hyperactivity disorder (ADHD), manifested in clinical features of inattention, hyperactivity, and impulsivity (
      American Psychiatric Association
      Diagnostic and Statistical Manual of Mental Disorders.
      ), is a common neurodevelopmental disorder persisting into adulthood and impacting 2.5% of adults worldwide (
      • Faraone S.V.
      • Asherson P.
      • Banaschewski T.
      • Biederman J.
      • Buitelaar J.K.
      • Ramos-Quiroga J.A.
      • et al.
      Attention-deficit/hyperactivity disorder.
      ,
      • Simon V.
      • Czobor P.
      • Bálint S.
      • Á Mészáros
      • Bitter I.
      Prevalence and correlates of adult attention-deficit hyperactivity disorder: Meta-analysis.
      ). Borderline personality disorder (BPD) is also characterized by behavioral impulsivity and affective dysregulation that contributes to interpersonal instability and severe stigma (
      • Leichsenring F.
      • Leibing E.
      • Kruse J.
      • New A.S.
      • Leweke F.
      Borderline personality disorder.
      ), with a prevalence of 1.3% in the general population (
      • Waluk O.R.
      • Youssef G.J.
      • Dowling N.A.
      The relationship between problem gambling and attention deficit hyperactivity disorder.
      ). ADHD and BPD frequently co-occur within the adult clinical population and in relatives owing to shared familial and heritable risk factors (
      • Kuja-Halkola R.
      • Lind Juto K.
      • Skoglund C.
      • Rück C.
      • Mataix-Cols D.
      • Pérez-Vigil A.
      • et al.
      Do borderline personality disorder and attention-deficit/hyperactivity disorder co-aggregate in families? A population-based study of 2 million Swedes.
      ,
      • Philipsen A.
      • Limberger M.F.
      • Lieb K.
      • Feige B.
      • Kleindienst N.
      • Ebner-Priemer U.
      • et al.
      Attention-deficit hyperactivity disorder as a potentially aggravating factor in borderline personality disorder.
      ). Approximately 38% of BPD cases have comorbid ADHD (
      • O’Malley G.K.
      • McHugh L.
      • Mac Giollabhui N.
      • Bramham J.
      Characterizing adult attention-deficit/hyperactivity-disorder and comorbid borderline personality disorder: ADHD symptoms, psychopathology, cognitive functioning and psychosocial factors.
      ), and the lifetime comorbidity of BPD is 33% among ADHD patients (
      • Bernardi S.
      • Faraone S.V.
      • Cortese S.
      • Kerridge B.T.
      • Pallanti S.
      • Wang S.
      • Blanco C.
      The lifetime impact of attention deficit hyperactivity disorder: Results from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC).
      ). Individuals diagnosed with childhood ADHD are at increased risk for BPD in adulthood (
      • Philipsen A.
      • Limberger M.F.
      • Lieb K.
      • Feige B.
      • Kleindienst N.
      • Ebner-Priemer U.
      • et al.
      Attention-deficit hyperactivity disorder as a potentially aggravating factor in borderline personality disorder.
      ), and the presence of BPD in individuals with ADHD may lead to unfavorable treatment outcome (
      • Tyrer P.
      • Reed G.M.
      • Crawford M.J.
      Classification, assessment, prevalence, and effect of personality disorder.
      ).
      Overlapping clinical characteristics of ADHD and BPD consist of impulsive behaviors and emotional instability (
      • Weiner L.
      • Perroud N.
      • Weibel S.
      Attention deficit hyperactivity disorder and borderline personality disorder in adults: A review of their links and risks.
      ,
      • Moukhtarian T.R.
      Investigation in the overlap of ADHD and borderline personality disorder: A multi-modal approach.
      ). Behavioral disinhibition has been recognized as a shared functional impairment in ADHD and BPD, in which impulsivity is a prominent psychopathological dimension characterized by premature and ill-considered actions in both conditions (
      • O’Malley G.K.
      • McHugh L.
      • Mac Giollabhui N.
      • Bramham J.
      Characterizing adult attention-deficit/hyperactivity-disorder and comorbid borderline personality disorder: ADHD symptoms, psychopathology, cognitive functioning and psychosocial factors.
      ,
      • Sebastian A.
      • Jung P.
      • Krause-Utz A.
      • Lieb K.
      • Schmahl C.
      • Tüscher O.
      Frontal dysfunctions of impulse control - A systematic review in borderline personality disorder and attention-deficit/hyperactivity disorder.
      ). Accordingly, anomalies of prefrontal-striatal activity reflected the neuropathological mechanism of impulsivity domain in ADHD (
      • Szekely E.
      • Sudre G.P.
      • Sharp W.
      • Leibenluft E.
      • Shaw P.
      Defining the neural substrate of the adult outcome of childhood ADHD: A multimodal neuroimaging study of response inhibition.
      ,
      • Buckholtz J.W.
      • Meyer-Lindenberg A.
      Psychopathology and the human connectome: Toward a transdiagnostic model of risk for mental illness.
      ), and disturbances in prefrontal-parietal circuitry might be responsible for response disinhibition when considering BPD samples (
      • Albert J.
      • López-Martín S.
      • Arza R.
      • Palomares N.
      • Hoyos S.
      • Carretié L.
      • et al.
      Response inhibition in borderline personality disorder: Neural and behavioral correlates.
      ). The other core area of symptomatic overlap is emotional instability—hasty and inflated alterations in affective states (
      • Moukhtarian T.R.
      • Mintah R.S.
      • Moran P.
      • Asherson P.
      Emotion dysregulation in attention-deficit/hyperactivity disorder and borderline personality disorder.
      ), centering on dysfunctional fronto-parieto-limbic systems (
      • Posner J.
      • Kass E.
      • Hulvershorn L.
      Using stimulants to treat ADHD-related emotional lability.
      ,
      • Morawetz C.
      • Riedel M.C.
      • Salo T.
      • Berboth S.
      • Eickhoff S.B.
      • Laird A.R.
      • Kohn N.
      Multiple large-scale neural networks underlying emotion regulation.
      ). Though sharing these similarities, individuals with BPD tend to have trouble controlling emotions related to their inner experience (
      • van Zutphen L.
      • Siep N.
      • Jacob G.A.
      • Goebel R.
      • Arntz A.
      Emotional sensitivity, emotion regulation and impulsivity in borderline personality disorder: A critical review of fMRI studies.
      ), while the affective and motoric hyperreactivity of ADHD patients is often a response to external events (
      • Philipsen A.
      Differential diagnosis and comorbidity of attention-deficit/hyperactivity disorder (ADHD) and borderline personality disorder (BPD) in adults.
      ,
      • Shaw P.
      • Stringaris A.
      • Nigg J.
      • Leibenluft E.
      Emotion dysregulation in attention deficit hyperactivity disorder.
      ).
      A neuroimaging meta-analysis of ADHD has suggested that volumetric reduction of the orbitofrontal and temporal cortices and blunted activation of inferior frontal gyrus (IFG), temporal, and striatal substrates have relevance to impairments in executive functions in adulthood (
      • Lukito S.
      • Norman L.
      • Carlisi C.
      • Radua J.
      • Hart H.
      • Simonoff E.
      • Rubia K.
      Comparative meta-analyses of brain structural and functional abnormalities during cognitive control in attention-deficit/hyperactivity disorder and autism spectrum disorder.
      ,
      • Cortese S.
      • Castellanos F.X.
      • Eickhoff C.R.
      • D’Acunto G.
      • Masi G.
      • Fox P.T.
      • et al.
      Functional decoding and meta-analytic connectivity modeling in adult attention-deficit/hyperactivity disorder.
      ). In BPD, gray matter reduction and hyperactivation of the amygdala contributed to disturbed emotion processing as a hallmark of BPD (
      • Schulze L.
      • Schmahl C.
      • Niedtfeld I.
      Neural correlates of disturbed emotion processing in borderline personality disorder: A multimodal meta-analysis.
      ), coupled with brain abnormalities in the orbitofrontal, dorsal prefrontal, and striatal regions linked to behavioral disinhibition (
      • Sebastian A.
      • Jung P.
      • Krause-Utz A.
      • Lieb K.
      • Schmahl C.
      • Tüscher O.
      Frontal dysfunctions of impulse control - A systematic review in borderline personality disorder and attention-deficit/hyperactivity disorder.
      ,
      • Zhang X.
      • Suo X.
      • Yang X.
      • Lai H.
      • Pan N.
      • He M.
      • et al.
      Structural and functional deficits and couplings in the cortico-striato-thalamo-cerebellar circuitry in social anxiety disorder.
      ). Thus, ADHD and BPD may have shared neural substrates apart from those behavioral similarities, indicating a transdiagnostic phenotype following a common psychopathological pathway (
      • Philipsen A.
      • Limberger M.F.
      • Lieb K.
      • Feige B.
      • Kleindienst N.
      • Ebner-Priemer U.
      • et al.
      Attention-deficit hyperactivity disorder as a potentially aggravating factor in borderline personality disorder.
      ,
      • Fusar-Poli P.
      • Solmi M.
      • Brondino N.
      • Davies C.
      • Chae C.
      • Politi P.
      • et al.
      Transdiagnostic psychiatry: A systematic review.
      ). Individuals with common neural markers may confer high risks for the comorbid condition, and additional attention is warranted to achieve a more effective treatment response in clinical management (
      • Weiner L.
      • Perroud N.
      • Weibel S.
      Attention deficit hyperactivity disorder and borderline personality disorder in adults: A review of their links and risks.
      ,
      • Ditrich I.
      • Philipsen A.
      • Matthies S.
      Borderline personality disorder (BPD) and attention deficit hyperactivity disorder (ADHD) revisited – a review-update on common grounds and subtle distinctions.
      ). Identifying their disjunctive neural mechanisms may potentially facilitate differential diagnosis. Regarding insufficient studies investigating their comorbidities and making comparisons directly (
      • O’Malley G.K.
      • McHugh L.
      • Mac Giollabhui N.
      • Bramham J.
      Characterizing adult attention-deficit/hyperactivity-disorder and comorbid borderline personality disorder: ADHD symptoms, psychopathology, cognitive functioning and psychosocial factors.
      ,
      • Moukhtarian T.R.
      • Reinhard I.
      • Morillas-Romero A.
      • Ryckaert C.
      • Mowlem F.
      • Bozhilova N.
      • et al.
      Wandering minds in attention-deficit/hyperactivity disorder and borderline personality disorder.
      ,
      • Rüsch N.
      • Weber M.
      • Il’yasov K.A.
      • Lieb K.
      • Ebert D.
      • Hennig J.
      • van Elst L.T.
      Inferior frontal white matter microstructure and patterns of psychopathology in women with borderline personality disorder and comorbid attention-deficit hyperactivity disorder.
      ), a systematic understanding of the conjunctive and disjunctive neural alterations with these disorders remains to be established. Overlapping and comparative meta-analytic approaches offer a promising approach for addressing this issue.
      Herein, we performed a multimodal meta-analysis of functional and structural neuroimaging studies to map the common and disorder-specific brain abnormalities in adults with ADHD and BPD (
      • Schulze L.
      • Schmahl C.
      • Niedtfeld I.
      Neural correlates of disturbed emotion processing in borderline personality disorder: A multimodal meta-analysis.
      ,
      • Norman L.J.
      • Carlisi C.
      • Lukito S.
      • Hart H.
      • Mataix-Cols D.
      • Radua J.
      • Rubia K.
      Structural and functional brain abnormalities in attention-deficit/hyperactivity disorder and obsessive-compulsive disorder: A comparative meta-analysis.
      ,
      • Albajes-Eizagirre A.
      • Solanes A.
      • Vieta E.
      • Radua J.
      Voxel-based meta-analysis via permutation of subject images (PSI): Theory and implementation for SDM.
      ). Obtained neural markers were then subjected to meta-analytic connectivity modeling (MACM) and data-driven functional decoding analyses to identify the associated neural circuit alterations and their relation to behavioral dimensions (
      • Li T.
      • Wang L.
      • Camilleri J.A.
      • Chen X.
      • Li S.
      • Stewart J.L.
      • et al.
      Mapping common grey matter volume deviation across child and adolescent psychiatric disorders.
      ,
      • Yarkoni T.
      • Poldrack R.A.
      • Nichols T.E.
      • Van Essen D.C.
      • Wager T.D.
      Large-scale automated synthesis of human functional neuroimaging data.
      ).

      Methods and Materials

      Literature Selection and Database Construction

      Prior to obtaining any dataset, we preregistered our meta-analytic plan on the Open Science Framework (registration doi: 10.17605/OSF.IO/KFZQN). We performed a comprehensive literature search in PubMed, Web of Science, and Embase for whole-brain functional magnetic resonance imaging (fMRI) or voxel-based morphometry (VBM) studies based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) criteria before March 2, 2021 (
      • Knobloch K.
      • Yoon U.
      • Vogt P.M.
      Preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement and publication bias.
      ). Studies were included in the meta-analysis if they compared adult patients with ADHD or BPD (ages 18–65 years) with healthy individuals on blood oxygen level–dependent signals or gray matter volume (GMV) (details of the search and article eligibility criteria in the Supplemental Methods).
      A total of 124 whole-brain articles were included in the current meta-analysis (ADHD-fMRI, 45 articles [proportion of cognitive/emotion experiments = 31/16]; BPD-fMRI, 52 articles [proportion of cognitive/emotion experiments = 19/33]; ADHD-VBM, 14 articles; BPD-VBM, 14 articles; one article reported parallel anatomic and functional findings and three articles enrolling duplicated samples were excluded in further analysis). These articles incorporated eligible observations from 3401 adult patients (ADHD, 1816; BPD, 1585) and 3238 healthy participants (procedures of literature search and included articles in Figure 1 and Table S1). All included studies were evaluated for quality and limitation to infer the importance of these findings with a 12-point Imaging Methodology Quality Assessment Checklist (
      • Shepherd A.M.
      • Matheson S.L.
      • Laurens K.R.
      • Carr V.J.
      • Green M.J.
      Systematic meta-analysis of insula volume in schizophrenia.
      ) (for details, see the Supplemental Methods).
      Figure thumbnail gr1
      Figure 1Flowcharts of the literature search and selection criteria for articles on attention-deficit/hyperactivity disorder (ADHD) and borderline personality disorder (BPD) in the meta-analysis. Panel (A) shows the literature search for articles on fMRI, and panel (B) for article on the VBM method. DTI, diffusion tensor imaging; fMRI, functional magnetic resonance imaging; ROI, region of interest; rs-fMRI, resting-state functional magnetic resonance imaging; SBM, surface-based morphometry; VBM, voxel-based morphometry.
      We extracted peak coordinates for brain functional or structural abnormalities and corresponding t values from those articles. Then, we coded each study with sample size, mean age, and sex ratio, as well as for parameters of scanner (i.e., tesla and slice thickness), statistical approach (i.e., kernel smoothing and multiple corrections), and reported clusters. For patient groups, the proportion receiving psychiatric medication and comorbid conditions were recorded. The task and corresponding Research Domain Criteria construct and domain were labeled for included fMRI studies (details of database construction in the Supplemental Methods and Table S2) (
      • Janiri D.
      • Moser D.A.
      • Doucet G.E.
      • Luber M.J.
      • Rasgon A.
      • Lee W.H.
      • et al.
      Shared neural phenotypes for mood and anxiety disorders: A meta-analysis of 226 task-related functional imaging studies.
      ). We pooled effect-size brain maps from all categories of fMRI experiments for the following rationales (
      • Janiri D.
      • Moser D.A.
      • Doucet G.E.
      • Luber M.J.
      • Rasgon A.
      • Lee W.H.
      • et al.
      Shared neural phenotypes for mood and anxiety disorders: A meta-analysis of 226 task-related functional imaging studies.
      ,
      • Chavanne A.V.
      • Robinson O.J.
      The overlapping neurobiology of induced and pathological anxiety: A meta-analysis of functional neural activation.
      ,
      • Müller V.I.
      • Cieslik E.C.
      • Serbanescu I.
      • Laird A.R.
      • Fox P.T.
      • Eickhoff S.B.
      Altered brain activity in unipolar depression revisited: Meta-analyses of neuroimaging studies.
      ). A task paradigm has the presumed association with particular psychological processing that attributes to specific neural substrates (
      • Janiri D.
      • Moser D.A.
      • Doucet G.E.
      • Luber M.J.
      • Rasgon A.
      • Lee W.H.
      • et al.
      Shared neural phenotypes for mood and anxiety disorders: A meta-analysis of 226 task-related functional imaging studies.
      ), while a brain region may underlie tasks of different categories (
      • Price C.J.
      • Friston K.J.
      Functional ontologies for cognition: The systematic definition of structure and function.
      ,
      • Pessoa L.
      Understanding brain networks and brain organization.
      ), indicating that not only one-to-many, but also many-to-one theory reflects the nature of the relationship between task paradigms and neural circuits. Pooling findings across experiments might be an objective approach that facilitates the comprehensive investigation of brain functional abnormalities of clinical populations (
      • Janiri D.
      • Moser D.A.
      • Doucet G.E.
      • Luber M.J.
      • Rasgon A.
      • Lee W.H.
      • et al.
      Shared neural phenotypes for mood and anxiety disorders: A meta-analysis of 226 task-related functional imaging studies.
      ,
      • van den Heuvel M.P.
      • Sporns O.
      A cross-disorder connectome landscape of brain dysconnectivity.
      ).

      Overlapping Meta-analysis

      To investigate brain functional and structural abnormalities of individuals with ADHD and BPD, we conducted a multimodal meta-analysis with Seed-based d Mapping with Permutation of Subject Images toolbox (SDM-PSI, version 6.21; https://www.sdmproject.com/). Voxelwise effect-size brain maps were reconstructed accommodating the peak coordinates and corresponding statistical values in the preprocessing (
      • Norman L.J.
      • Carlisi C.
      • Lukito S.
      • Hart H.
      • Mataix-Cols D.
      • Radua J.
      • Rubia K.
      Structural and functional brain abnormalities in attention-deficit/hyperactivity disorder and obsessive-compulsive disorder: A comparative meta-analysis.
      ,
      • Albajes-Eizagirre A.
      • Solanes A.
      • Fullana M.A.
      • Ioannidis J.P.A.
      • Fusar-Poli P.
      • Torrent C.
      • et al.
      Meta-analysis of voxel-based neuroimaging studies using seed-based d mapping with permutation of subject images (SDM-PSI).
      ). We created mean effect-size maps across studies and experiments with covarying age and sex in random-effect general linear models separately for modalities and disorders to estimate the meta-analytical case-control difference (
      • Radua J.
      • Mataix-Cols D.
      • Phillips M.L.
      • El-Hage W.
      • Kronhaus D.M.
      • Cardoner N.
      • Surguladze S.
      A new meta-analytic method for neuroimaging studies that combines reported peak coordinates and statistical parametric maps.
      ).
      To evaluate the converging neural substrates within and across disorders and imaging modalities, we performed the overlapping analysis following separate disorder- and modality-specific analyses to obtain conjunctive and comparative findings (
      • Chavanne A.V.
      • Robinson O.J.
      The overlapping neurobiology of induced and pathological anxiety: A meta-analysis of functional neural activation.
      ,
      • Radua J.
      • Romeo M.
      • Mataix-Cols D.
      • Fusar-Poli P.
      A general approach for combining voxel-based meta-analyses conducted in different neuroimaging modalities.
      ). A comparative coordinate-based meta-analytic approach could identify both common and distinct patterns of two disorders, and it has the potential to inform the emergence of diagnostic biomarkers for comorbid conditions (
      • Norman L.J.
      • Carlisi C.
      • Lukito S.
      • Hart H.
      • Mataix-Cols D.
      • Radua J.
      • Rubia K.
      Structural and functional brain abnormalities in attention-deficit/hyperactivity disorder and obsessive-compulsive disorder: A comparative meta-analysis.
      ,
      • Wise T.
      • Radua J.
      • Via E.
      • Cardoner N.
      • Abe O.
      • Adams T.M.
      • et al.
      Common and distinct patterns of grey-matter volume alteration in major depression and bipolar disorder: Evidence from voxel-based meta-analysis.
      ). To avoid the likelihood that the false positive rate is higher than a preferred degree in the worst-case scenario, the SDM adjusts the raw union of probabilities to the desired threshold (
      • Radua J.
      • Romeo M.
      • Mataix-Cols D.
      • Fusar-Poli P.
      A general approach for combining voxel-based meta-analyses conducted in different neuroimaging modalities.
      ). To threshold the statistical maps of neural abnormalities of ADHD and BPD, we applied the familywise error rate correction with threshold-free cluster enhancement statistics at p < .05 and cluster size >10 voxels to control for multiple comparisons and obtain robust findings (detailed introduction of SDM-PSI and procedures of overlapping analysis in the Supplemental Methods) (
      • Albajes-Eizagirre A.
      • Solanes A.
      • Vieta E.
      • Radua J.
      Voxel-based meta-analysis via permutation of subject images (PSI): Theory and implementation for SDM.
      ).

      Meta-analytic Connectivity Modeling

      Meta-analytic coactivation analysis examines regions that typically are coactive in prior imaging research with those found to be abnormal in a clinical or psychophysiological study (
      • Laird A.R.
      • Eickhoff S.B.
      • Li K.
      • Robin D.A.
      • Glahn D.C.
      • Fox P.T.
      Investigating the functional heterogeneity of the default mode network using coordinate-based meta-analytic modeling.
      ,
      • Jakobs O.
      • Langner R.
      • Caspers S.
      • Roski C.
      • Cieslik E.C.
      • Zilles K.
      • et al.
      Across-study and within-subject functional connectivity of a right temporo-parietal junction subregion involved in stimulus-context integration.
      ), allowing for a practical interpretation of the interaction across the whole brain and adding complementary information to task-related and resting-state functional connectivity studies (
      • Li T.
      • Wang L.
      • Camilleri J.A.
      • Chen X.
      • Li S.
      • Stewart J.L.
      • et al.
      Mapping common grey matter volume deviation across child and adolescent psychiatric disorders.
      ,
      • Chen T.
      • Becker B.
      • Camilleri J.
      • Wang L.
      • Yu S.
      • Eickhoff S.B.
      • Feng C.
      A domain-general brain network underlying emotional and cognitive interference processing: Evidence from coordinate-based and functional connectivity meta-analyses.
      ,
      • Nooner K.B.
      • Colcombe S.J.
      • Tobe R.H.
      • Mennes M.
      • Benedict M.M.
      • Moreno A.L.
      • et al.
      The NKI-Rockland Sample: A model for accelerating the pace of discovery science in psychiatry.
      ). We delineated the coactivated neural circuits of identified peaks by extracting the peak coordinates and integrated findings of articles that reported activation in the seed regions to yield a convergent coactivation brain map in the Neurosynth package (http://www.neurosynth.org) (
      • Robinson J.L.
      • Laird A.R.
      • Glahn D.C.
      • Lovallo W.R.
      • Fox P.T.
      Metaanalytic connectivity modeling: Delineating the functional connectivity of the human amygdala.
      ). A 5-mm sphere was created for peaks of the identified clusters to search the candidate coactivated brain regions with the false discovery rate correction (p < .01) to reduce the false positive connectomes (
      • Li T.
      • Wang L.
      • Camilleri J.A.
      • Chen X.
      • Li S.
      • Stewart J.L.
      • et al.
      Mapping common grey matter volume deviation across child and adolescent psychiatric disorders.
      ). To characterize neural coactivation at the large-scale network level, we overlaid the coactivation patterns into the default mode network (DMN), central executive network (CEN), dorsal attention network, ventral attention network (VAN), somatomotor network, visual network, and cortical affective network (
      • Yeo B.T.T.
      • Krienen F.M.
      • Sepulcre J.
      • Sabuncu M.R.
      • Lashkari D.
      • Hollinshead M.
      • et al.
      The organization of the human cerebral cortex estimated by intrinsic functional connectivity.
      ,
      • Castellanos F.X.
      • Proal E.
      Large-scale brain systems in ADHD: Beyond the prefrontal-striatal model.
      ) and calculated the similarity of coactivation patterns to these networks (introduction of Neurosynth and details of MACM and related analysis in the Supplemental Methods) (
      • Li T.
      • Wang L.
      • Camilleri J.A.
      • Chen X.
      • Li S.
      • Stewart J.L.
      • et al.
      Mapping common grey matter volume deviation across child and adolescent psychiatric disorders.
      ).

      Functional Decoding

      Functional decoding analysis allows for a data-driven understanding to bridge the gap between psychophysiological functioning and MRI-derived brain alterations (
      • Yarkoni T.
      • Poldrack R.A.
      • Nichols T.E.
      • Van Essen D.C.
      • Wager T.D.
      Large-scale automated synthesis of human functional neuroimaging data.
      ,
      • Poldrack R.A.
      Inferring mental states from neuroimaging data: From reverse inference to large-scale decoding.
      ). We used Neurosynth to decode the pooled neural anomalies, as it currently contains a large-scale neuroimaging database and encodes articles with interactive psychological terms (
      • Yarkoni T.
      • Poldrack R.A.
      • Nichols T.E.
      • Van Essen D.C.
      • Wager T.D.
      Large-scale automated synthesis of human functional neuroimaging data.
      ). The Pearson correlation coefficients of psychological terms were calculated between the term-based activation maps and our modeled brain maps (
      • Poldrack R.A.
      Inferring mental states from neuroimaging data: From reverse inference to large-scale decoding.
      ), and we recorded the terms with the top 10 highest correlation coefficients (
      • Ge R.
      • Liu X.
      • Long D.
      • Frangou S.
      • Vila-Rodriguez F.
      Sex effects on cortical morphological networks in healthy young adults.
      ). Furthermore, we gathered the correlation coefficients of five behavioral domains (i.e., action, cognition, emotion, interoception, and perception) derived from a paradigm taxonomy and presented with their relative proportions (
      • Fox P.T.
      • Laird A.R.
      • Fox S.P.
      • Fox P.M.
      • Uecker A.M.
      • Crank M.
      • et al.
      BrainMap taxonomy of experimental design: Description and evaluation.
      ,
      • Poeppl T.B.
      • Donges M.R.
      • Mokros A.
      • Rupprecht R.
      • Fox P.T.
      • Laird A.R.
      • et al.
      A view behind the mask of sanity: Meta-analysis of aberrant brain activity in psychopaths.
      ) to determine the most prominent behavioral domain related to suprathreshold brain regions (details of functional decoding and behavioral domains in the Supplemental Methods and Table S3).

      Ancillary Analyses

      We conducted separate disorder- and modality-specific analyses to supplement with overlapping analysis. Meta-regression analysis was employed to examine the potential role of demographic factors and medication status at the whole-brain level (
      • Lukito S.
      • Norman L.
      • Carlisi C.
      • Radua J.
      • Hart H.
      • Simonoff E.
      • Rubia K.
      Comparative meta-analyses of brain structural and functional abnormalities during cognitive control in attention-deficit/hyperactivity disorder and autism spectrum disorder.
      ). Following the main meta-analyses, we extracted the Hedges’ g imputations for peak coordinates of clusters and performed Spearman’s correlation, Mann-Whitney U, and Kruskal-Wallis H tests to explore the modulatory effects of confounding variables (
      • Janiri D.
      • Moser D.A.
      • Doucet G.E.
      • Luber M.J.
      • Rasgon A.
      • Lee W.H.
      • et al.
      Shared neural phenotypes for mood and anxiety disorders: A meta-analysis of 226 task-related functional imaging studies.
      ). For fMRI studies, we calculated the contribution of included experiments attributed to each Research Domain Criteria domain for identified clusters based on Hedges’ g imputations. Additionally, we conducted cognition and emotion subgroup analyses to investigate specific brain functional profiles. Publication bias and between-study heterogeneity were assessed via funnel plots, Egger’s test, and I2 statistics (details of ancillary analyses in the Supplemental Methods) (
      • Radua J.
      • Mataix-Cols D.
      • Phillips M.L.
      • El-Hage W.
      • Kronhaus D.M.
      • Cardoner N.
      • Surguladze S.
      A new meta-analytic method for neuroimaging studies that combines reported peak coordinates and statistical parametric maps.
      ).

      Results

      Sample Characteristics

      Among 122 eligible studies in the meta-analysis, no statistical difference between patient groups was noted in the sample size-weighted t tests in age (fMRI: t91 = 0.510, p = .611; VBM: t25 = 0.052, p = .959), but BPD studies consisted of a larger proportion of females than ADHD studies in both modalities (fMRI: t91 = 11.027, p < .001; VBM: t25 = 5.621, p < .001). The proportion of medicated patients did not differ by diagnosis (χ21 = 0.020, p = .889) (Table 1).
      Table 1Characteristics of Included Sample
      ADHDHCADHD-matchedBPDHCBPD-matched
      fMRI Modality
       Number of articles4552
       Total sample size98195311101081
       Age, mean, years27.928.029.028.3
       Female, %
      For the proportion of females in whole sample.
      32.7%37.4%86.3%86.3%
       Taking medication, %
      For the proportion of medicated patients 2 weeks before scanning.
      27.9%34.7%
      sMRI Modality
       Number of articles1414
       Total sample size835724475480
       Mean age, years30.431.629.929.5
       Female, %
      For the proportion of females in whole sample.
      41.9%50.0%85.5%83.3%
       Taking medication, %
      For the proportion of medicated patients 2 weeks before scanning.
      38.2%27.8%
      Characteristics are reported based on eligible studies excluding three articles with duplicated samples, and one article on ADHD reported parallel anatomic and functional findings.
      ADHD, attention-deficit/hyperactivity disorder; BPD, borderline personality disorder; HC, healthy control; fMRI, functional magnetic resonance imaging; sMRI, structural magnetic resonance imaging.
      a For the proportion of females in whole sample.
      b For the proportion of medicated patients 2 weeks before scanning.

      Overlapping Analysis Across Disorders

      The overlapping analysis indicated that functional abnormalities of the left inferior parietal lobule (IPL) were conjunctive in ADHD and BPD cases (peak coordinates: −40, −50, 52; Z = 4.454; k = 23; shared hypoactivation pattern), and those of the left anterior superior temporal gyrus (STG)/temporal pole (peak coordinates: −48, −2, −14; Z = 3.688; k = 82) and right posterior STG (peak coordinates: 52, −38, 18; Z = 4.255; k = 44) (Table 2 and Figure 2) were disjunctive (reduced response in ADHD but hyperactivation in BPD). The meta-analytic connectivity of the left IPL mainly overlaid on the CEN (% relative distribution [%RD]: 12.13%), the left STG on the DMN (%RD: 15.49%), and the right STG on the VAN (%RD: 10.60%) (Figure 2; Table S5). The convergent findings of the left IPL and right STG were predominantly correlated with the action domain (% correlation coefficient: 47.33% and 27.61%, respectively), while the left STG/temporal pole was linked to the cognition domain (% correlation coefficient: 39.82%) (Figure 2; Table S6). No conjunctive or disjunctive brain structural abnormalities were found between ADHD and BPD, though both structural patterns involved a part of the left orbitofrontal cortex (OFC) that had different specific anatomic locations (Table 3).
      Table 2Common and Distinct Brain Abnormalities of ADHD and BPD
      Contrast/Brain RegionBAMNI Coordinates (x, y, z)Zp ValueCluster Size
      (ADHD vs. HC) vs. (BPD vs. HC) in fMRI
       Conjunction: L inferior parietal lobule40−40, −50, 524.454.02623
       Disjunction: L anterior superior temporal gyrus/temporal pole21−48, −2, −143.688.00182
       Disjunction: R posterior superior temporal gyrus4252, −38, 184.255.00244
      (ADHD vs. HC) vs. (BPD vs. HC) in sMRI
       Disjunction or conjunction: None
      fMRI vs. sMRI in ADHD
       Conjunction: R putamen4830, 10, 4−4.585.003145
      fMRI vs. sMRI in BPD
       Conjunction: L orbitofrontal/anterior cingulate cortex11−4, 34, −10−4.044.01656
      Suprathreshold clusters were identified at FWE rate correction with pFWE < .05 and cluster size >10 voxels. Breakdown clusters are shown in Table S4.
      ADHD, attention-deficit/hyperactivity disorder; BA, Brodmann area; BPD, borderline personality disorder; fMRI, functional magnetic resonance imaging; FWE, familywise error; HC, healthy control; L, left; MNI, Montreal Neurological Institute; R, right; sMRI, structural magnetic resonance imaging.
      Figure thumbnail gr2
      Figure 2Overlapping functional patterns across attention-deficit/hyperactivity disorder and borderline personality disorder. (A) Transdiagnostic clusters in the left inferior parietal lobule (L. IPL) (conjunctive findings) and bilateral superior temporal gyrus (STG) (left STG [L. STG] and right STG [R. STG]) (disjunctive findings). Statistical brain maps are available online at https://osf.io/jsdgq/files/. (B) Similarity of coactivation pattern to large-scale network. Additional details in . (C) Contribution of each behavioral domain to each cluster in functional decoding. Additional details in . AFN, cortical affective network; CEN, central executive network; DAN, dorsal attention network; DMN, default mode network; SMN, somatomotor network; VAN, ventral attention network; VN, visual network.
      Table 3Disorder-Specific Brain Abnormalities of ADHD and BPD
      Contrast/Brain RegionBAMNI Coordinates (x, y, z)Zp ValueCluster Size
      ADHD vs. HC in fMRI
       HC > ADHD
      L inferior frontal gyrus/insula/superior temporal gyrus47/48−52, 12, 106.163.0013626
      R inferior frontal gyrus/insula/superior temporal gyrus38/47/4832, 18, −26.178.0012165
      R inferior parietal lobule39/4044, −58, 505.628.002654
      L supplementary motor area6−2, 4, 585.382.001632
      R cerebellum3736, −50, −384.541.002380
      L orbitofrontal cortex10−38, 58, −24.373.01880
      L inferior parietal lobule40−40, −48, 485.110.01252
      BPD vs. HC in fMRI
       BPD > HC
      R superior temporal gyrus22/42/4858, −30, 185.475.0011386
      L superior/middle temporal gyrus/temporal pole20/21/38−46, 2, −225.927.0011297
      R parahippocampal gyrus/amygdala28/34/3628, −4, −286.293.001590
      R precuneus2316, −68, 265.886.001164
      L superior frontal gyrus10−8, 58, 125.039.01865
       HC > BPD
      L superior/inferior parietal lobule7/40−26, −54, 585.469.001350
      L ventral anterior cingulate cortex11/25−6, 32, −64.937.009160
      Cerebellum2, −60, −384.592.04212
      ADHD vs. HC in sMRI
       HC > ADHD
      R putamen4828, 4, 85.722.001420
      L orbitofrontal cortex110, 46, −265.060.02757
      R supramarginal gyrus4062, −44, 344.329.04522
      BPD vs. HC in sMRI
       HC > BPD
      L orbitofrontal cortex11−6, 40, −145.134.002540
      L middle occipital gyrus17/18−20, −98, 45.118.007190
      Suprathreshold clusters were identified at FWE rate correction with pFWE < .05 and cluster size >10 voxels. Breakdown clusters are shown in Table S4.
      ADHD, attention-deficit/hyperactivity disorder; BA, Brodmann area; BPD, borderline personality disorder; fMRI, functional magnetic resonance imaging; FWE, familywise error; HC, healthy control; L, left; MNI, Montreal Neurological Institute; R, right; sMRI, structural magnetic resonance imaging.

      Overlapping Analysis Across Modalities

      In ADHD, multimodal conjunction analysis revealed that the cluster in the right putamen (peak coordinates: 30, 10, 4; Z = 4.585; k = 145) exhibited both blunted functional activation and decreased GMV in contrast with control subjects, while those of the left OFC/anterior cingulate cortex (ACC) converged in multimodality in individuals with BPD (peak coordinates: −4, 34, −10; Z = 4.044; k = 56) (Table 2 and Figure 3). When overlaying the findings of MACM analysis, the coactivation pattern of the right putamen was largely located in the VAN and that of the left OFC in the DMN (%RD: 11.79% and 23.15%, respectively) (Figure 3; Table S5). The multimodal abnormality of ADHD was predominantly associated with the action domain, while that of BPD was associated with the perception domain (% correlation coefficient: 48.75% and 36.53%, respectively) (Figure 3; Table S6). No disjunctive findings in multimodality were found in either ADHD or BPD.
      Figure thumbnail gr3
      Figure 3Divergent multimodal abnormalities of attention-deficit/hyperactivity disorder (ADHD) and borderline personality disorder (BPD). (A) ADHD in multimodal clusters of the right putamen (R. putamen) and BPD in the left orbitofrontal cortex (L. OFC). Statistical brain maps are available online at https://osf.io/jsdgq/files/. (B) Similarity of coactivated pattern to large-scale network. Additional details in . (C) Contribution of each behavioral domain to each cluster in functional decoding. Additional details in . AFN, cortical affective network; CEN, central executive network; DAN, dorsal attention network; DMN, default mode network; SMN, somatomotor network; VAN, ventral attention network; VN, visual network.

      Ancillary Analyses

      Separate analyses of ADHD cases alone compared with healthy individuals demonstrated hypoactivation in regions including the bilateral IFG/insula/STG, bilateral IPL, left supplementary motor area, right cerebellum, and left OFC and GMV reduction in the right putamen, left OFC, and right supramarginal gyrus (SMG). Patients with BPD exhibited hyperactivation in the bilateral STG, right precuneus, left superior frontal gyrus, and right parahippocampal gyrus/amygdala; reduced activation in left superior parietal lobule/IPL and ventral ACC; and GMV reduction in the left OFC and left middle occipital gyrus relative to control subjects (Table 3). Coactivation patterns in MACM analysis are shown in Table S5 and Figure S1, as anomalies of ADHD mainly coactivated with the CEN, while those of BPD linked with the DMN. Functional decoding analysis revealed that most of the identified clusters of ADHD were primarily associated with the action domain, while those of BPD were diverse (Table S6 and Figure S2). The top 10 psychophysical terms correlated with those brain abnormalities to infer its main functioning are shown in Table S7, as the left IPL of overlapping analysis predominately correlated with “retention” and “execution,” the left STG correlated with “music” and “speak,” the right STG correlated with “pain” and “listening,” and the right putamen, from distinct multimodal analysis, correlated with “sensation” and “motor” and left OFC with “decision” and “value.”
      Meta-regression analysis revealed that sex modulated the neuropathological processing in the right putamen in ADHD and that in the left cerebellum and right ACC in BPD. Additionally, the medication status modulated structural alterations of the right putamen, SMG, and left OFC in ADHD, but no significant clusters were identified in the fMRI modality. In BPD, medication status was associated with structural patterns in the right insula and functional patterns in the left amygdala (details and findings of age in Table S8). The modulatory effects of medication status on brain structural patterns of ADHD and BPD overlapped in the right putamen/insula. Among confounding variables of interests, sex and age played critical roles in identified clusters (20 out of 25 clusters and 7 out of 25 clusters, respectively) (Table S9). The Research Domain Criteria domain of cognitive systems was prevalent in functional profiles of ADHD (all contributions >62%) and in that of negative valence systems in profiles of BPD (all contributions >38%), and the differences of contributions did not reach statistical significance within disorders for any clusters (Table S10). In subgroup analysis for fMRI studies, ADHD samples exhibited hypoactivations in the right cerebellum, bilateral insula, left supplementary motor area, left superior frontal gyrus, right IPL, and right striatum relative to healthy control subjects in the cognition category, while BPD cases showed functional abnormalities in the right parahippocampal gyrus, right STG, right inferior temporal gyrus, and left ACC in the emotion category (Table S11). Interstudy heterogeneity was not significant among all identified clusters according to I2 statistics (all I2 < 10%) (details and publication bias results in Table S12).

      Discussion

      The current multimodal meta-analysis provides a comprehensive delineation of the similarities and differences in the neural bases of ADHD and BPD in adulthood underlying their shared and distinct clinical features. Following up on overlapping functional patterns centered on the left IPL and bilateral STG, the identified clusters coactivated with the CEN, DMN, and VAN in the large-scale MACM analysis and data-driven functional decoding indicated that these patterns mainly corresponded to the action and cognition domains. Structural abnormalities of both disorders emerged in the left OFC, yet without shared profiles. The multimodal meta-analysis demonstrated that converging brain functional and structural alterations were located in the right putamen in ADHD and in the left OFC in BPD, with linkage to the VAN and DMN and predominant involvement of the action and perception domains, respectively. These findings shed light on the similarities and divergences of brain alterations associated with ADHD and BPD in multimodal dimensions, which may facilitate mechanistic understanding of the two disorders, provide potential information about the basis for their shared behavioral features, and in the longer term, potentially facilitate differential diagnosis and provide interventional targets for treatment discovery programs.

      Common Neural Abnormalities in ADHD and BPD

      The overlapping functional abnormality in the left IPL may be responsible for the psychopathological model of the comorbid condition of ADHD and BPD in clinical settings. The prior model was constructed based on the view that behavioral disinhibition derived from alterations in the frontostriatal circuitry and attention deficits were triggered by those in frontoparietal systems (
      • Arnsten A.F.T.T.
      • Rubia K.
      Neurobiological circuits regulating attention, cognitive control, motivation, and emotion: Disruptions in neurodevelopmental psychiatric disorders.
      ). However, considering symptomatic overlaps of ADHD and BPD, we rephrased the model given that the reduced activation of the left IPL is linked with deficits in attention (
      • Hart H.
      • Radua J.
      • Nakao T.
      • Mataix-Cols D.
      • Rubia K.
      Meta-analysis of functional magnetic resonance imaging studies of inhibition and attention in attention-deficit/hyperactivity disorder: Exploring task-specific, stimulant medication, and age effects.
      ), which serve as the bases of disinhibitory control and impulsivity in healthy populations, individuals with ADHD, and individuals with BPD (
      • Buckholtz J.W.
      • Meyer-Lindenberg A.
      Psychopathology and the human connectome: Toward a transdiagnostic model of risk for mental illness.
      ,
      • Schulze L.
      • Schmahl C.
      • Niedtfeld I.
      Neural correlates of disturbed emotion processing in borderline personality disorder: A multimodal meta-analysis.
      ,
      • Puiu A.A.
      • Wudarczyk O.
      • Kohls G.
      • Bzdok D.
      • Herpertz-Dahlmann B.
      • Konrad K.
      Meta-analytic evidence for a joint neural mechanism underlying response inhibition and state anger.
      ,
      • Tiego J.
      • Testa R.
      • Bellgrove M.A.
      • Pantelis C.
      • Whittle S.
      A hierarchical model of inhibitory control.
      ). As a key node in the CEN (
      • Power J.D.
      • Cohen A.L.
      • Nelson S.M.
      • Wig G.S.
      • Barnes K.A.
      • Church J.A.
      • et al.
      Functional network organization of the human brain.
      ), the IPL serves as a multidimensional integrator binding cognitive and affective information processing (
      • Gottlieb J.
      From thought to action: The parietal cortex as a bridge between perception, action, and cognition.
      ,
      • Buschman T.J.
      • Miller E.K.
      Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices.
      ). Abnormalities of the IPL may indicate the executive network disruption in the neuropathological processing underlying cognitive control and decision making (
      • Menon V.
      Large-scale brain networks and psychopathology: A unifying triple network model.
      ). Findings of the left IPL in a transdiagnostic approach may contribute to problems of reduced attentional control and behavioral disinhibition that are associated with both ADHD and BPD (
      • Faraone S.V.
      • Asherson P.
      • Banaschewski T.
      • Biederman J.
      • Buitelaar J.K.
      • Ramos-Quiroga J.A.
      • et al.
      Attention-deficit/hyperactivity disorder.
      ,
      • Buckholtz J.W.
      • Meyer-Lindenberg A.
      Psychopathology and the human connectome: Toward a transdiagnostic model of risk for mental illness.
      ,
      • Gavazzi G.
      • Rossi A.
      • Orsolini S.
      • Diciotti S.
      • Giovannelli F.
      • Salvadori E.
      • et al.
      Impulsivity trait and proactive cognitive control: An fMRI study.
      ).
      The overlapping but disjunctive functional patterns in the bilateral STG have relevance to the cognition and action domains, and their coactivated brain regions are largely instantiated in VAN and DMN. A hypoactivated STG/temporal pole in adults with ADHD impinged on cognition as a result of dysfunctional temporal processing (
      • Cortese S.
      • Castellanos F.X.
      • Eickhoff C.R.
      • D’Acunto G.
      • Masi G.
      • Fox P.T.
      • et al.
      Functional decoding and meta-analytic connectivity modeling in adult attention-deficit/hyperactivity disorder.
      ), and a hyperresponsive bilateral STG has been linked to impaired cognitive processing (
      • Schulze L.
      • Schmahl C.
      • Niedtfeld I.
      Neural correlates of disturbed emotion processing in borderline personality disorder: A multimodal meta-analysis.
      ,
      • Ruocco A.C.
      • Amirthavasagam S.
      • Choi-kain L.W.
      • Mcmain S.F.
      Neural correlates of negative emotionality in borderline personality disorder: An activation-likelihood-estimation meta-Analysis.
      ). Specifically, regions of the STG engage in action observation that contributes to regulating motor actions and impulsivity (
      • Pan N.
      • Wang S.
      • Zhao Y.
      • Lai H.
      • Qin K.
      • Li J.
      • et al.
      Brain gray matter structures associated with trait impulsivity: A systematic review and voxel-based meta-analysis.
      ,
      • Van Overwalle F.
      • Baetens K.
      Understanding others’ actions and goals by mirror and mentalizing systems: A meta-analysis.
      ,
      • Ing A.
      • Sämann P.G.
      • Chu C.
      • Tay N.
      • Biondo F.
      • Robert G.
      • et al.
      Identification of neurobehavioural symptom groups based on shared brain mechanisms.
      ), and the STG has connections with limbic systems underlying the integration of emotion processing (
      • Olson I.R.
      • Plotzker A.
      • Ezzyat Y.
      The enigmatic temporal pole: A review of findings on social and emotional processing.
      ). As above, although functional abnormalities of ADHD and BPD overlapped in the STG and were anchored to the cognition and action realms, the inverse activation direction suggests diverse psychopathological mechanisms in the two disorders (
      • Sebastian A.
      • Retz W.
      • Tüscher O.
      • Turner D.
      Violent offending in borderline personality disorder and attention deficit/hyperactivity disorder.
      ,
      • Lampe K.
      • Konrad K.
      • Kroener S.
      • Fast K.
      • Kunert H.J.
      • Herpertz S.C.
      Neuropsychological and behavioural disinhibition in adult ADHD compared to borderline personality disorder.
      ). Clarifying the differential behavioral effects of these two patterns of abnormality may represent a promising direction for future studies (
      • Lai H.
      • Kong X.
      • Zhao Y.
      • Pan N.
      • Zhang X.
      • He M.
      • et al.
      Patterns of a structural covariance network associated with dispositional optimism during late adolescence.
      ).
      Both ADHD and BPD cases exhibited a GMV decrease in the medial OFC that coactivated with the DMN, yielding different locations (the gyrus rectus and part of the ACC, respectively) and functional decoding (interoception and emotion domains). One role of the OFC is to flexibly adjust adaptive behaviors through reward processing to optimize goal-directed action (
      • Maia T.V.
      • McClelland J.L.
      A reexamination of the evidence for the somatic marker hypothesis: What participants really know in the Iowa gambling task.
      ,
      • Jonker F.A.
      • Jonker C.
      • Scheltens P.
      • Scherder E.J.A.
      The role of the orbitofrontal cortex in cognition and behavior.
      ), confined to neural correlates of trait impulsivity (
      • Pan N.
      • Wang S.
      • Zhao Y.
      • Lai H.
      • Qin K.
      • Li J.
      • et al.
      Brain gray matter structures associated with trait impulsivity: A systematic review and voxel-based meta-analysis.
      ). For correlated psychophysical terms in Neurosynth, the OFC cluster of ADHD was mainly associated with “craving” and that of BPD with “value” and “decision.” Thus, while the OFC is impacted in both disorders, somewhat different regions are involved with potential differences in behavioral aspect (
      American Psychiatric Association
      Diagnostic and Statistical Manual of Mental Disorders.
      ,
      • Moukhtarian T.R.
      • Reinhard I.
      • Morillas-Romero A.
      • Ryckaert C.
      • Mowlem F.
      • Bozhilova N.
      • et al.
      Wandering minds in attention-deficit/hyperactivity disorder and borderline personality disorder.
      ). The lack of converging structural alterations seems in conflict with the common phenomenon that ADHD co-occurs with BPD (
      • O’Malley G.K.
      • McHugh L.
      • Mac Giollabhui N.
      • Bramham J.
      Characterizing adult attention-deficit/hyperactivity-disorder and comorbid borderline personality disorder: ADHD symptoms, psychopathology, cognitive functioning and psychosocial factors.
      ), regarding the fact that neuroimaging researchers selectively and exclusively enroll ADHD and BPD cases to emphasize case-control differences at the expense of the representativeness (
      • Lukito S.
      • Norman L.
      • Carlisi C.
      • Radua J.
      • Hart H.
      • Simonoff E.
      • Rubia K.
      Comparative meta-analyses of brain structural and functional abnormalities during cognitive control in attention-deficit/hyperactivity disorder and autism spectrum disorder.
      ).

      Distinct Multimodal Abnormalities in ADHD Versus BPD

      The divergent brain functional and structural alterations of ADHD and BPD were located in the right putamen and left OFC/ACC, respectively. The connectivity between the OFC and striatum accounts for reinforcement learning and behavioral flexibility (
      • Buckholtz J.W.
      • Meyer-Lindenberg A.
      Psychopathology and the human connectome: Toward a transdiagnostic model of risk for mental illness.
      ,
      • Balleine B.W.
      • O’Doherty J.P.
      Human and rodent homologies in action control: Corticostriatal determinants of goal-directed and habitual action.
      ). Notably, high trait impulsivity, as a transdiagnostic symptom shared by ADHD and BPD, has been associated with decreased functional activation and integrity of myelination in circuitry including the medial OFC/ACC and striatum (
      • Ziegler G.
      • Hauser T.U.
      • Moutoussis M.
      • Bullmore E.T.
      • Goodyer I.M.
      • Fonagy P.
      • et al.
      Compulsivity and impulsivity traits linked to attenuated developmental frontostriatal myelination trajectories.
      ,
      • Diekhof E.K.
      • Nerenberg L.
      • Falkai P.
      • Dechent P.
      • Baudewig J.J.
      • Gruber O.
      • Dechent P.
      Impulsive personality and the ability to resist immediate reward: An fMRI study examining interindividual differences in the neural mechanisms underlying self-control.
      ). In the MACM analysis, the coactivated spatial pattern of the right putamen for ADHD cases is closely aligned with the VAN, which partially explains their maladaptive cognitive and emotional processing of external stimuli and dysfunctions of inhibitory control in ADHD (
      • Menon V.
      Large-scale brain networks and psychopathology: A unifying triple network model.
      ). The coactivated network of the left OFC for BPD individuals was most similar to DMN, which may account for the associated loss of dynamic control for attention reorienting between internal and external events (
      • Vossel S.
      • Weidner R.
      • Driver J.
      • Friston K.J.
      • Fink G.R.
      Deconstructing the architecture of dorsal and ventral attention systems with dynamic causal modeling.
      ). Functional decoding indicated that the cluster of the putamen was anchored on the action domain and the OFC on the perception domain. Striatal alterations in individuals with ADHD may contribute to their dysfunction in the bottom-up processing of behavioral control regarding its role in the action realm (
      • Norman L.J.
      • Carlisi C.
      • Lukito S.
      • Hart H.
      • Mataix-Cols D.
      • Radua J.
      • Rubia K.
      Structural and functional brain abnormalities in attention-deficit/hyperactivity disorder and obsessive-compulsive disorder: A comparative meta-analysis.
      ,
      • Lenzi F.
      • Cortese S.
      • Harris J.
      • Masi G.
      Neuroscience and Biobehavioral Reviews Pharmacotherapy of emotional dysregulation in adults with ADHD : A systematic review and meta-analysis.
      ), while the abnormal OFC pattern in individuals with BPD underlies their processing emotional events, alexithymia, and integrating emotional experience with adaptive action planning—potentially contributing to their increased risk for impulsive self-injury (
      • Niedtfeld I.
      • Schulze L.
      • Kirsch P.
      • Herpertz S.C.
      • Bohus M.
      • Schmahl C.
      Affect regulation and pain in borderline personality disorder: A possible link to the understanding of self-injury.
      ,
      • Schnell K.
      • Dietrich T.
      • Schnitker R.
      • Daumann J.
      • Herpertz S.C.
      Processing of autobiographical memory retrieval cues in borderline personality disorder.
      ). Notably, our disjunctive findings should be interpreted with caution regarding the possibility that differences in task paradigms restricted us from a comprehensive understanding toward neural substrates of ADHD and BPD, and regarding the possibility that those distinct abnormalities would be identified in both disorders as more unbiased evidence emerged.

      Disorder-Specific Abnormalities in ADHD and BPD

      In functional alteration patterns, individuals with ADHD showed hypoactivation in the bilateral IFG/insula/STG. Psychological functions of the IFG have been generally recognized as a brake on affective processing and behavioral actions (
      • Aron A.R.
      • Robbins T.W.
      • Poldrack R.A.
      Inhibition and the right inferior frontal cortex: One decade on.
      ,
      • Bari A.
      • Robbins T.W.
      • Andrea Q.
      • Robbins T.W.
      • Bari A.
      • Robbins T.W.
      Inhibition and impulsivity: Behavioral and neural basis of response control.
      ), which is of clinical relevance for the dysfunction of inhibition and executive control in ADHD (
      • Barkley R.A.
      Behavioral inhibition, sustained attention, and executive functions: Constructing a unifying theory of ADHD.
      ). Among individuals with ADHD and comorbid BPD, microstructural abnormalities of IFG co-occurring with limbic alterations were linked to dysfunctional emotion regulation and other behavioral features (
      • Rüsch N.
      • Weber M.
      • Il’yasov K.A.
      • Lieb K.
      • Ebert D.
      • Hennig J.
      • van Elst L.T.
      Inferior frontal white matter microstructure and patterns of psychopathology in women with borderline personality disorder and comorbid attention-deficit hyperactivity disorder.
      ). In BPD cases, abnormalities were found in the left superior frontal gyrus, superior parietal lobule, and right parahippocampal gyrus/amygdala. Psychological functions of frontal components correspond to top-down cognitive control and emotion regulation, of parietal ones to behavioral integration and social mentalizing, and of limbic substrates to salience detection and emotion processing (
      • Buckholtz J.W.
      • Meyer-Lindenberg A.
      Psychopathology and the human connectome: Toward a transdiagnostic model of risk for mental illness.
      ,
      • van Zutphen L.
      • Siep N.
      • Jacob G.A.
      • Goebel R.
      • Arntz A.
      Emotional sensitivity, emotion regulation and impulsivity in borderline personality disorder: A critical review of fMRI studies.
      ). These regions together comprise a fronto-parieto-limbic circuitry that accounts for emotion regulation (
      • Morawetz C.
      • Riedel M.C.
      • Salo T.
      • Berboth S.
      • Eickhoff S.B.
      • Laird A.R.
      • Kohn N.
      Multiple large-scale neural networks underlying emotion regulation.
      ). In addition, patterns of GMV reduction were also found in brain regions of the right putamen and SMG in ADHD and the left middle occipital gyrus in BPD. Given the predominant linkage with the action domain, the SMG contributes to action reprogramming for rapid adaptation to external alterations and to maintaining the memory flow of serial orders (
      • Hartwigsen G.
      • Siebner H.R.
      Joint contribution of left dorsal premotor cortex and supramarginal gyrus to rapid action reprogramming.
      ,
      • Guidali G.
      • Pisoni A.
      • Bolognini N.
      • Papagno C.
      Keeping order in the brain: The supramarginal gyrus and serial order in short-term memory.
      ). Apart from relating to visuospatial functioning, the middle occipital gyrus processes the category-selective attention via facial recognition (
      • Tu S.
      • Qiu J.
      • Martens U.
      • Zhang Q.
      Category-selective attention modulates unconscious processes in the middle occipital gyrus.
      ). Notably, large-scale mega-analyses revealed that reduced volume of subcortical regions was not found in adults, but rather was found only in children with ADHD compared with typically developing control subjects, suggesting a neuropathological model of altered subcortical trajectories (
      • Hoogman M.
      • Muetzel R.
      • Guimaraes J.P.
      • Shumskaya E.
      • Mennes M.
      • Zwiers M.P.
      • et al.
      Brain imaging of the cortex in ADHD: A coordinated analysis of large-scale clinical and population-based samples.
      ,
      • Hoogman M.
      • Bralten J.
      • Hibar D.P.
      • Mennes M.
      • Zwiers M.P.
      • Schweren L.S.J.
      • et al.
      Subcortical brain volume differences in participants with attention deficit hyperactivity disorder in children and adults: A cross-sectional mega-analysis.
      ).

      Potential Role of Medication Status

      The meta-regression analysis revealed that modulated brain structural alterations of ADHD and BPD overlapped in the right putamen/insula pertaining to current medication treatment. The putamen is a potential therapeutic target for ADHD given its role in behavioral inhibition and control (
      • Albajara Sáenz A.
      • Villemonteix T.
      • Massat I.
      • Aenz A.A.S.
      • Villemonteix T.
      • Massat I.
      Structural and functional neuroimaging in attention-deficit/hyperactivity disorder.
      ), and psychotropic treatment was linked to neural responses in the insula in individuals with BPD (
      • Marceau E.M.
      • Meuldijk D.
      • Townsend M.L.
      • Solowij N.
      • Grenyer B.F.S.S.
      Biomarker correlates of psychotherapy outcomes in borderline personality disorder: A systematic review.
      ). While individual relation of imaging to particular medications is not possible for a meta-analysis such as ours, these findings highlight the potential value of studying how treatments typically provided to patients with ADHD and patients with BPD may modulate these particular brain regions to reduce behavioral disturbances.

      Limitations

      First, our findings may not generalize to pediatric or geriatric populations. Because pediatric BPD is not well studied compared with pediatric ADHD, only articles recruiting adult participants were included in the meta-analysis. Second, the representativeness of our transdiagnostic findings might be weakened due to study issues such as differences in sex ratios or differences in the use of emotional and cognitive fMRI task paradigms between the two disorders of interest. Differences in sex ratios of included samples result from different susceptibility of sex populations in community samples (
      • Leichsenring F.
      • Leibing E.
      • Kruse J.
      • New A.S.
      • Leweke F.
      Borderline personality disorder.
      ,
      • Ramtekkar U.P.
      • Reiersen A.M.
      • Todorov A.A.
      • Todd R.D.
      Sex and age differences in attention-deficit/hyperactivity disorder symptoms and diagnoses: Implications for DSM-V and ICD-11.
      ), and various predominant clinical manifestations contribute to biased research interest and differences in task paradigms (
      • O’Malley G.K.
      • McHugh L.
      • Mac Giollabhui N.
      • Bramham J.
      Characterizing adult attention-deficit/hyperactivity-disorder and comorbid borderline personality disorder: ADHD symptoms, psychopathology, cognitive functioning and psychosocial factors.
      ). Adding sex to covariates in the meta-analysis may reconcile the concern, but further studies accounting for the general clinical presentation of those disorders might be of great interest. Finally, data to permit direct association of brain alterations and behavior at the individual level were not available, so we used data-driven approaches to provide suggestive behavioral linkages with our regional brain findings.

      Conclusions

      The current meta-analysis, to our knowledge, is the first to investigate the multimodal transdiagnostic neural patterns of ADHD and BPD, and it includes data-driven functional decoding and meta-analytic connectivity approaches. Our study depicts a comprehensive presentation of common and distinct neural bases of ADHD and BPD, which indicates shared abnormal patterns in the temporoparietal profile and distinct patterns in frontostriatal circuitry. These findings provide novel transdiagnostic information regarding two psychiatric conditions that share clinical features of behavioral disinhibition and affective reactivity (
      • O’Malley G.K.
      • McHugh L.
      • Mac Giollabhui N.
      • Bramham J.
      Characterizing adult attention-deficit/hyperactivity-disorder and comorbid borderline personality disorder: ADHD symptoms, psychopathology, cognitive functioning and psychosocial factors.
      ). Understanding of the distinct disrupted neurocircuits with each disorder could help establish better pathophysiological models and facilitate differential diagnosis. Further, the transdiagnostic and disorder-specific neural bases of illness have the potential to serve as therapeutic targets in the development of novel treatments for ADHD and BPD, and to better understand mechanisms for their high comorbidity rates and overlapping symptom profiles (
      • Albajara Sáenz A.
      • Villemonteix T.
      • Massat I.
      • Aenz A.A.S.
      • Villemonteix T.
      • Massat I.
      Structural and functional neuroimaging in attention-deficit/hyperactivity disorder.
      ,
      • Marceau E.M.
      • Meuldijk D.
      • Townsend M.L.
      • Solowij N.
      • Grenyer B.F.S.S.
      Biomarker correlates of psychotherapy outcomes in borderline personality disorder: A systematic review.
      ), which is one of the goals of psychoradiology (
      • Lui S.
      • Zhou X.
      • Sweeney J.
      • Gong Q.
      Psychoradiology: The frontier of neuroimaging in psychiatry.
      ,
      • Gong Q.
      ,
      • Suo X.
      • Zuo C.
      • Lan H.
      • Pan N.
      • Zhang X.
      • Kemp G.J.
      • et al.
      COVID-19 vicarious traumatization links functional connectome to general distress.
      ,
      • Li F.
      • Sun H.
      • Biswal B.B.
      • Sweeney J.A.
      • Gong Q.
      Artificial intelligence applications in psychoradiology.
      ).

      Acknowledgments and Disclosures

      This work was supported by the National Natural Science Foundation of China (Grant Nos. 81621003 [to QG], 82027808 [to QG], and 31800963 [to SW]), the National Natural Science Foundation (Grant No. 81820108018 [to JAS and QG]), and the Key Research and Development Project of Science and Technology at Department of Sichuan Province (Grant No. 2021YFS0242 [to LL]).
      We deeply appreciate all the authors of the included studies who responded to our requests for further information.
      JAS is a consultant for VeraSci. All other authors report no biomedical financial interests or potential conflicts of interest.
      Protocol preregistration: Open Science Framework: https://osf.io/kfzqn.

      Supplementary Material

      References

        • American Psychiatric Association
        Diagnostic and Statistical Manual of Mental Disorders.
        5th ed. American Psychiatric Press, Washington, DC2013
        • Faraone S.V.
        • Asherson P.
        • Banaschewski T.
        • Biederman J.
        • Buitelaar J.K.
        • Ramos-Quiroga J.A.
        • et al.
        Attention-deficit/hyperactivity disorder.
        Nat Rev Dis Prim. 2015; 115020
        • Simon V.
        • Czobor P.
        • Bálint S.
        • Á Mészáros
        • Bitter I.
        Prevalence and correlates of adult attention-deficit hyperactivity disorder: Meta-analysis.
        Br J Psychiatry. 2009; 194: 204-211
        • Leichsenring F.
        • Leibing E.
        • Kruse J.
        • New A.S.
        • Leweke F.
        Borderline personality disorder.
        Lancet. 2011; 377: 74-84
        • Waluk O.R.
        • Youssef G.J.
        • Dowling N.A.
        The relationship between problem gambling and attention deficit hyperactivity disorder.
        J Gambl Stud. 2016; 32: 591-604
        • Kuja-Halkola R.
        • Lind Juto K.
        • Skoglund C.
        • Rück C.
        • Mataix-Cols D.
        • Pérez-Vigil A.
        • et al.
        Do borderline personality disorder and attention-deficit/hyperactivity disorder co-aggregate in families? A population-based study of 2 million Swedes.
        Mol Psychiatry. 2018; : 341-349
        • Philipsen A.
        • Limberger M.F.
        • Lieb K.
        • Feige B.
        • Kleindienst N.
        • Ebner-Priemer U.
        • et al.
        Attention-deficit hyperactivity disorder as a potentially aggravating factor in borderline personality disorder.
        Br J Psychiatry. 2008; 192: 118-123
        • O’Malley G.K.
        • McHugh L.
        • Mac Giollabhui N.
        • Bramham J.
        Characterizing adult attention-deficit/hyperactivity-disorder and comorbid borderline personality disorder: ADHD symptoms, psychopathology, cognitive functioning and psychosocial factors.
        Eur Psychiatry. 2016; 31: 29-36
        • Bernardi S.
        • Faraone S.V.
        • Cortese S.
        • Kerridge B.T.
        • Pallanti S.
        • Wang S.
        • Blanco C.
        The lifetime impact of attention deficit hyperactivity disorder: Results from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC).
        Psychol Med. 2012; 42: 875-887
        • Tyrer P.
        • Reed G.M.
        • Crawford M.J.
        Classification, assessment, prevalence, and effect of personality disorder.
        Lancet. 2015; 385: 717-726
        • Weiner L.
        • Perroud N.
        • Weibel S.
        Attention deficit hyperactivity disorder and borderline personality disorder in adults: A review of their links and risks.
        Neuropsychiatr Dis Treat. 2019; 15: 3115-3129
        • Moukhtarian T.R.
        Investigation in the overlap of ADHD and borderline personality disorder: A multi-modal approach.
        Doctoral Thesis, King’s College London, United Kingdom2019
        • Sebastian A.
        • Jung P.
        • Krause-Utz A.
        • Lieb K.
        • Schmahl C.
        • Tüscher O.
        Frontal dysfunctions of impulse control - A systematic review in borderline personality disorder and attention-deficit/hyperactivity disorder.
        Front Hum Neurosci. 2014; 8: 698
        • Szekely E.
        • Sudre G.P.
        • Sharp W.
        • Leibenluft E.
        • Shaw P.
        Defining the neural substrate of the adult outcome of childhood ADHD: A multimodal neuroimaging study of response inhibition.
        Am J Psychiatry. 2017; 174: 867-876
        • Buckholtz J.W.
        • Meyer-Lindenberg A.
        Psychopathology and the human connectome: Toward a transdiagnostic model of risk for mental illness.
        Neuron. 2012; 74: 990-1004
        • Albert J.
        • López-Martín S.
        • Arza R.
        • Palomares N.
        • Hoyos S.
        • Carretié L.
        • et al.
        Response inhibition in borderline personality disorder: Neural and behavioral correlates.
        Biol Psychol. 2019; 143: 32-40
        • Moukhtarian T.R.
        • Mintah R.S.
        • Moran P.
        • Asherson P.
        Emotion dysregulation in attention-deficit/hyperactivity disorder and borderline personality disorder.
        Borderline Personal Disord Emot Dysregulation. 2018; 5: 9
        • Posner J.
        • Kass E.
        • Hulvershorn L.
        Using stimulants to treat ADHD-related emotional lability.
        Curr Psychiatry Rep. 2014; 16: 478
        • Morawetz C.
        • Riedel M.C.
        • Salo T.
        • Berboth S.
        • Eickhoff S.B.
        • Laird A.R.
        • Kohn N.
        Multiple large-scale neural networks underlying emotion regulation.
        Neurosci Biobehav Rev. 2020; 116: 382-395
        • van Zutphen L.
        • Siep N.
        • Jacob G.A.
        • Goebel R.
        • Arntz A.
        Emotional sensitivity, emotion regulation and impulsivity in borderline personality disorder: A critical review of fMRI studies.
        Neurosci Biobehav Rev. 2015; 51: 64-76
        • Philipsen A.
        Differential diagnosis and comorbidity of attention-deficit/hyperactivity disorder (ADHD) and borderline personality disorder (BPD) in adults.
        Eur Arch Psychiatry Clin Neurosci. 2006; 256: i42-i46
        • Shaw P.
        • Stringaris A.
        • Nigg J.
        • Leibenluft E.
        Emotion dysregulation in attention deficit hyperactivity disorder.
        Am J Psychiatry. 2014; 171: 276-293
        • Lukito S.
        • Norman L.
        • Carlisi C.
        • Radua J.
        • Hart H.
        • Simonoff E.
        • Rubia K.
        Comparative meta-analyses of brain structural and functional abnormalities during cognitive control in attention-deficit/hyperactivity disorder and autism spectrum disorder.
        Psychol Med. 2020; 50: 894-919
        • Cortese S.
        • Castellanos F.X.
        • Eickhoff C.R.
        • D’Acunto G.
        • Masi G.
        • Fox P.T.
        • et al.
        Functional decoding and meta-analytic connectivity modeling in adult attention-deficit/hyperactivity disorder.
        Biol Psychiatry. 2016; 80: 896-904
        • Schulze L.
        • Schmahl C.
        • Niedtfeld I.
        Neural correlates of disturbed emotion processing in borderline personality disorder: A multimodal meta-analysis.
        Biol Psychiatry. 2016; 79: 97-106
        • Zhang X.
        • Suo X.
        • Yang X.
        • Lai H.
        • Pan N.
        • He M.
        • et al.
        Structural and functional deficits and couplings in the cortico-striato-thalamo-cerebellar circuitry in social anxiety disorder.
        Transl Psychiatr. 2022; 1226
        • Fusar-Poli P.
        • Solmi M.
        • Brondino N.
        • Davies C.
        • Chae C.
        • Politi P.
        • et al.
        Transdiagnostic psychiatry: A systematic review.
        World Psychiatry. 2019; 18: 192-207
        • Ditrich I.
        • Philipsen A.
        • Matthies S.
        Borderline personality disorder (BPD) and attention deficit hyperactivity disorder (ADHD) revisited – a review-update on common grounds and subtle distinctions.
        Borderline Personal Disord Emot Dysregulation. 2021; 8: 22
        • Moukhtarian T.R.
        • Reinhard I.
        • Morillas-Romero A.
        • Ryckaert C.
        • Mowlem F.
        • Bozhilova N.
        • et al.
        Wandering minds in attention-deficit/hyperactivity disorder and borderline personality disorder.
        Eur Neuropsychopharmacol. 2020; 38: 98-109
        • Rüsch N.
        • Weber M.
        • Il’yasov K.A.
        • Lieb K.
        • Ebert D.
        • Hennig J.
        • van Elst L.T.
        Inferior frontal white matter microstructure and patterns of psychopathology in women with borderline personality disorder and comorbid attention-deficit hyperactivity disorder.
        Neuroimage. 2007; 35: 738-747
        • Norman L.J.
        • Carlisi C.
        • Lukito S.
        • Hart H.
        • Mataix-Cols D.
        • Radua J.
        • Rubia K.
        Structural and functional brain abnormalities in attention-deficit/hyperactivity disorder and obsessive-compulsive disorder: A comparative meta-analysis.
        JAMA Psychiatry. 2016; 73: 815-825
        • Albajes-Eizagirre A.
        • Solanes A.
        • Vieta E.
        • Radua J.
        Voxel-based meta-analysis via permutation of subject images (PSI): Theory and implementation for SDM.
        Neuroimage. 2019; 186: 174-184
        • Li T.
        • Wang L.
        • Camilleri J.A.
        • Chen X.
        • Li S.
        • Stewart J.L.
        • et al.
        Mapping common grey matter volume deviation across child and adolescent psychiatric disorders.
        Neurosci Biobehav Rev. 2020; 115: 273-284
        • Yarkoni T.
        • Poldrack R.A.
        • Nichols T.E.
        • Van Essen D.C.
        • Wager T.D.
        Large-scale automated synthesis of human functional neuroimaging data.
        Nat Methods. 2011; 8: 665-670
        • Knobloch K.
        • Yoon U.
        • Vogt P.M.
        Preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement and publication bias.
        J Craniomaxillofacial Surg. 2011; 39: 91-92
        • Shepherd A.M.
        • Matheson S.L.
        • Laurens K.R.
        • Carr V.J.
        • Green M.J.
        Systematic meta-analysis of insula volume in schizophrenia.
        Biol Psychiatry. 2012; 72: 775-784
        • Janiri D.
        • Moser D.A.
        • Doucet G.E.
        • Luber M.J.
        • Rasgon A.
        • Lee W.H.
        • et al.
        Shared neural phenotypes for mood and anxiety disorders: A meta-analysis of 226 task-related functional imaging studies.
        JAMA Psychiatry. 2020; 77: 172-179
        • Chavanne A.V.
        • Robinson O.J.
        The overlapping neurobiology of induced and pathological anxiety: A meta-analysis of functional neural activation.
        Am J Psychiatry. 2021; 178: 156-164
        • Müller V.I.
        • Cieslik E.C.
        • Serbanescu I.
        • Laird A.R.
        • Fox P.T.
        • Eickhoff S.B.
        Altered brain activity in unipolar depression revisited: Meta-analyses of neuroimaging studies.
        JAMA Psychiatry. 2017; 74: 47-55
        • Price C.J.
        • Friston K.J.
        Functional ontologies for cognition: The systematic definition of structure and function.
        Cogn Neuropsychol. 2005; 22: 262-275
        • Pessoa L.
        Understanding brain networks and brain organization.
        Phys Life Rev. 2014; 11: 400-435
        • van den Heuvel M.P.
        • Sporns O.
        A cross-disorder connectome landscape of brain dysconnectivity.
        Nat Rev Neurosci. 2019; 20: 435-446
        • Albajes-Eizagirre A.
        • Solanes A.
        • Fullana M.A.
        • Ioannidis J.P.A.
        • Fusar-Poli P.
        • Torrent C.
        • et al.
        Meta-analysis of voxel-based neuroimaging studies using seed-based d mapping with permutation of subject images (SDM-PSI).
        J Vis Exp. 2019; 15359841
        • Radua J.
        • Mataix-Cols D.
        • Phillips M.L.
        • El-Hage W.
        • Kronhaus D.M.
        • Cardoner N.
        • Surguladze S.
        A new meta-analytic method for neuroimaging studies that combines reported peak coordinates and statistical parametric maps.
        Eur Psychiatry. 2012; 27: 605-611
        • Radua J.
        • Romeo M.
        • Mataix-Cols D.
        • Fusar-Poli P.
        A general approach for combining voxel-based meta-analyses conducted in different neuroimaging modalities.
        Curr Med Chem. 2013; 20: 462-466
        • Wise T.
        • Radua J.
        • Via E.
        • Cardoner N.
        • Abe O.
        • Adams T.M.
        • et al.
        Common and distinct patterns of grey-matter volume alteration in major depression and bipolar disorder: Evidence from voxel-based meta-analysis.
        Mol Psychiatry. 2017; 22: 1455-1463
        • Laird A.R.
        • Eickhoff S.B.
        • Li K.
        • Robin D.A.
        • Glahn D.C.
        • Fox P.T.
        Investigating the functional heterogeneity of the default mode network using coordinate-based meta-analytic modeling.
        J Neurosci. 2009; 29: 14496-14505
        • Jakobs O.
        • Langner R.
        • Caspers S.
        • Roski C.
        • Cieslik E.C.
        • Zilles K.
        • et al.
        Across-study and within-subject functional connectivity of a right temporo-parietal junction subregion involved in stimulus-context integration.
        Neuroimage. 2012; 60: 2389-2398
        • Chen T.
        • Becker B.
        • Camilleri J.
        • Wang L.
        • Yu S.
        • Eickhoff S.B.
        • Feng C.
        A domain-general brain network underlying emotional and cognitive interference processing: Evidence from coordinate-based and functional connectivity meta-analyses.
        Brain Struct Funct. 2018; 223: 3813-3840
        • Nooner K.B.
        • Colcombe S.J.
        • Tobe R.H.
        • Mennes M.
        • Benedict M.M.
        • Moreno A.L.
        • et al.
        The NKI-Rockland Sample: A model for accelerating the pace of discovery science in psychiatry.
        Front Neurosci. 2012; 6: 152
        • Robinson J.L.
        • Laird A.R.
        • Glahn D.C.
        • Lovallo W.R.
        • Fox P.T.
        Metaanalytic connectivity modeling: Delineating the functional connectivity of the human amygdala.
        Hum Brain Mapp. 2010; 31: 173-184
        • Yeo B.T.T.
        • Krienen F.M.
        • Sepulcre J.
        • Sabuncu M.R.
        • Lashkari D.
        • Hollinshead M.
        • et al.
        The organization of the human cerebral cortex estimated by intrinsic functional connectivity.
        J Neurophysiol. 2011; 106: 1125-1165
        • Castellanos F.X.
        • Proal E.
        Large-scale brain systems in ADHD: Beyond the prefrontal-striatal model.
        Trends Cogn Sci. 2012; 16: 17-26
        • Poldrack R.A.
        Inferring mental states from neuroimaging data: From reverse inference to large-scale decoding.
        Neuron. 2011; 72: 692-697
        • Ge R.
        • Liu X.
        • Long D.
        • Frangou S.
        • Vila-Rodriguez F.
        Sex effects on cortical morphological networks in healthy young adults.
        Neuroimage. 2021; 233117945
        • Fox P.T.
        • Laird A.R.
        • Fox S.P.
        • Fox P.M.
        • Uecker A.M.
        • Crank M.
        • et al.
        BrainMap taxonomy of experimental design: Description and evaluation.
        Hum Brain Mapp. 2005; 25: 185-198
        • Poeppl T.B.
        • Donges M.R.
        • Mokros A.
        • Rupprecht R.
        • Fox P.T.
        • Laird A.R.
        • et al.
        A view behind the mask of sanity: Meta-analysis of aberrant brain activity in psychopaths.
        Mol Psychiatry. 2019; 24: 463-470
        • Arnsten A.F.T.T.
        • Rubia K.
        Neurobiological circuits regulating attention, cognitive control, motivation, and emotion: Disruptions in neurodevelopmental psychiatric disorders.
        J Am Acad Child Adolesc Psychiatry. 2012; 51: 356-367
        • Hart H.
        • Radua J.
        • Nakao T.
        • Mataix-Cols D.
        • Rubia K.
        Meta-analysis of functional magnetic resonance imaging studies of inhibition and attention in attention-deficit/hyperactivity disorder: Exploring task-specific, stimulant medication, and age effects.
        JAMA Psychiatry. 2013; 70: 185-198
        • Puiu A.A.
        • Wudarczyk O.
        • Kohls G.
        • Bzdok D.
        • Herpertz-Dahlmann B.
        • Konrad K.
        Meta-analytic evidence for a joint neural mechanism underlying response inhibition and state anger.
        Hum Brain Mapp. 2020; 41: 3147-3160
        • Tiego J.
        • Testa R.
        • Bellgrove M.A.
        • Pantelis C.
        • Whittle S.
        A hierarchical model of inhibitory control.
        Front Psychol. 2018; 9: 1339
        • Power J.D.
        • Cohen A.L.
        • Nelson S.M.
        • Wig G.S.
        • Barnes K.A.
        • Church J.A.
        • et al.
        Functional network organization of the human brain.
        Neuron. 2011; 72: 665-678
        • Gottlieb J.
        From thought to action: The parietal cortex as a bridge between perception, action, and cognition.
        Neuron. 2007; 53: 9-16
        • Buschman T.J.
        • Miller E.K.
        Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices.
        Science. 2007; 315: 1860-1862
        • Menon V.
        Large-scale brain networks and psychopathology: A unifying triple network model.
        Trends Cogn Sci. 2011; 15: 483-506
        • Gavazzi G.
        • Rossi A.
        • Orsolini S.
        • Diciotti S.
        • Giovannelli F.
        • Salvadori E.
        • et al.
        Impulsivity trait and proactive cognitive control: An fMRI study.
        Eur J Neurosci. 2019; 49: 1171-1179
        • Ruocco A.C.
        • Amirthavasagam S.
        • Choi-kain L.W.
        • Mcmain S.F.
        Neural correlates of negative emotionality in borderline personality disorder: An activation-likelihood-estimation meta-Analysis.
        Biol Psychiatry. 2013; 73: 153-160
        • Pan N.
        • Wang S.
        • Zhao Y.
        • Lai H.
        • Qin K.
        • Li J.
        • et al.
        Brain gray matter structures associated with trait impulsivity: A systematic review and voxel-based meta-analysis.
        Hum Brain Mapp. 2021; 42: 2214-2235
        • Van Overwalle F.
        • Baetens K.
        Understanding others’ actions and goals by mirror and mentalizing systems: A meta-analysis.
        Neuroimage. 2009; 48: 564-584
        • Ing A.
        • Sämann P.G.
        • Chu C.
        • Tay N.
        • Biondo F.
        • Robert G.
        • et al.
        Identification of neurobehavioural symptom groups based on shared brain mechanisms.
        Nat Hum Behav. 2019; 3: 1306-1318
        • Olson I.R.
        • Plotzker A.
        • Ezzyat Y.
        The enigmatic temporal pole: A review of findings on social and emotional processing.
        Brain. 2007; 130: 1718-1731
        • Sebastian A.
        • Retz W.
        • Tüscher O.
        • Turner D.
        Violent offending in borderline personality disorder and attention deficit/hyperactivity disorder.
        Neuropharmacology. 2019; 156107565
        • Lampe K.
        • Konrad K.
        • Kroener S.
        • Fast K.
        • Kunert H.J.
        • Herpertz S.C.
        Neuropsychological and behavioural disinhibition in adult ADHD compared to borderline personality disorder.
        Psychol Med. 2007; 37: 1717-1729
        • Lai H.
        • Kong X.
        • Zhao Y.
        • Pan N.
        • Zhang X.
        • He M.
        • et al.
        Patterns of a structural covariance network associated with dispositional optimism during late adolescence.
        Neuroimage. 2022; 251119009
        • Maia T.V.
        • McClelland J.L.
        A reexamination of the evidence for the somatic marker hypothesis: What participants really know in the Iowa gambling task.
        Proc Natl Acad Sci U S A. 2004; 101: 16075-16080
        • Jonker F.A.
        • Jonker C.
        • Scheltens P.
        • Scherder E.J.A.
        The role of the orbitofrontal cortex in cognition and behavior.
        Rev Neurosci. 2015; 26: 1-11
        • Balleine B.W.
        • O’Doherty J.P.
        Human and rodent homologies in action control: Corticostriatal determinants of goal-directed and habitual action.
        Neuropsychopharmacology. 2010; 35: 48-69
        • Ziegler G.
        • Hauser T.U.
        • Moutoussis M.
        • Bullmore E.T.
        • Goodyer I.M.
        • Fonagy P.
        • et al.
        Compulsivity and impulsivity traits linked to attenuated developmental frontostriatal myelination trajectories.
        Nat Neurosci. 2019; 22: 992-999
        • Diekhof E.K.
        • Nerenberg L.
        • Falkai P.
        • Dechent P.
        • Baudewig J.J.
        • Gruber O.
        • Dechent P.
        Impulsive personality and the ability to resist immediate reward: An fMRI study examining interindividual differences in the neural mechanisms underlying self-control.
        Hum Brain Mapp. 2012; 33: 2768-2784
        • Vossel S.
        • Weidner R.
        • Driver J.
        • Friston K.J.
        • Fink G.R.
        Deconstructing the architecture of dorsal and ventral attention systems with dynamic causal modeling.
        J Neurosci. 2012; 32: 10637-10648
        • Lenzi F.
        • Cortese S.
        • Harris J.
        • Masi G.
        Neuroscience and Biobehavioral Reviews Pharmacotherapy of emotional dysregulation in adults with ADHD : A systematic review and meta-analysis.
        Neurosci Biobehav Rev. 2018; 84: 359-367
        • Niedtfeld I.
        • Schulze L.
        • Kirsch P.
        • Herpertz S.C.
        • Bohus M.
        • Schmahl C.
        Affect regulation and pain in borderline personality disorder: A possible link to the understanding of self-injury.
        Biol Psychiatry. 2010; 68: 383-391
        • Schnell K.
        • Dietrich T.
        • Schnitker R.
        • Daumann J.
        • Herpertz S.C.
        Processing of autobiographical memory retrieval cues in borderline personality disorder.
        J Affect Disord. 2007; 97: 253-259
        • Aron A.R.
        • Robbins T.W.
        • Poldrack R.A.
        Inhibition and the right inferior frontal cortex: One decade on.
        Trends Cogn Sci. 2014; 18: 177-185
        • Bari A.
        • Robbins T.W.
        • Andrea Q.
        • Robbins T.W.
        • Bari A.
        • Robbins T.W.
        Inhibition and impulsivity: Behavioral and neural basis of response control.
        Prog Neurobiol. 2013; 108: 44-79
        • Barkley R.A.
        Behavioral inhibition, sustained attention, and executive functions: Constructing a unifying theory of ADHD.
        Psychol Bull. 1997; 121: 65-94
        • Hartwigsen G.
        • Siebner H.R.
        Joint contribution of left dorsal premotor cortex and supramarginal gyrus to rapid action reprogramming.
        Brain Stimul. 2015; 8: 945-952
        • Guidali G.
        • Pisoni A.
        • Bolognini N.
        • Papagno C.
        Keeping order in the brain: The supramarginal gyrus and serial order in short-term memory.
        Cortex. 2019; 119: 89-99
        • Tu S.
        • Qiu J.
        • Martens U.
        • Zhang Q.
        Category-selective attention modulates unconscious processes in the middle occipital gyrus.
        Conscious Cogn. 2013; 22: 479-485
        • Hoogman M.
        • Muetzel R.
        • Guimaraes J.P.
        • Shumskaya E.
        • Mennes M.
        • Zwiers M.P.
        • et al.
        Brain imaging of the cortex in ADHD: A coordinated analysis of large-scale clinical and population-based samples.
        Am J Psychiatry. 2019; 176: 531-542
        • Hoogman M.
        • Bralten J.
        • Hibar D.P.
        • Mennes M.
        • Zwiers M.P.
        • Schweren L.S.J.
        • et al.
        Subcortical brain volume differences in participants with attention deficit hyperactivity disorder in children and adults: A cross-sectional mega-analysis.
        Lancet Psychiatry. 2017; 4: 310-319
        • Albajara Sáenz A.
        • Villemonteix T.
        • Massat I.
        • Aenz A.A.S.
        • Villemonteix T.
        • Massat I.
        Structural and functional neuroimaging in attention-deficit/hyperactivity disorder.
        Dev Med Child Neurol. 2019; 61: 399-405
        • Marceau E.M.
        • Meuldijk D.
        • Townsend M.L.
        • Solowij N.
        • Grenyer B.F.S.S.
        Biomarker correlates of psychotherapy outcomes in borderline personality disorder: A systematic review.
        Neurosci Biobehav Rev. 2018; 94: 166-178
        • Ramtekkar U.P.
        • Reiersen A.M.
        • Todorov A.A.
        • Todd R.D.
        Sex and age differences in attention-deficit/hyperactivity disorder symptoms and diagnoses: Implications for DSM-V and ICD-11.
        J Am Acad Child Adolesc Psychiatry. 2010; 49: 213-217
        • Lui S.
        • Zhou X.
        • Sweeney J.
        • Gong Q.
        Psychoradiology: The frontier of neuroimaging in psychiatry.
        Radiology. 2016; 281: 357-372
        • Gong Q.
        Psychoradiology: Neuroimaging Clinics of North America. New York: Elsevier Inc, 2020: 1-123
        • Suo X.
        • Zuo C.
        • Lan H.
        • Pan N.
        • Zhang X.
        • Kemp G.J.
        • et al.
        COVID-19 vicarious traumatization links functional connectome to general distress.
        Neuroimage. 2022; 255119185
        • Li F.
        • Sun H.
        • Biswal B.B.
        • Sweeney J.A.
        • Gong Q.
        Artificial intelligence applications in psychoradiology.
        Psychoradiology. 2021; 1: 94-107