Integrated analysis of gray and white matter alterations in attention-deficit/hyperactivity disorder

Local structural and functional MRI markers of compulsive behaviors and obsessive-compulsive disorder diagnosis within striatum-based circuits

BACKGROUND

Obsessive-compulsive disorder (OCD) is a classic disorder on the compulsivity spectrum, with diverse comorbidities. In the current study, we sought to understand OCD from a dimensional perspective by identifying multimodal neuroimaging patterns correlated with multiple phenotypic characteristics within the striatum-based circuits known to be affected by OCD.

METHODS

Neuroimaging measurements of local functional and structural features and clinical information were collected from 110 subjects, including 51 patients with OCD and 59 healthy control subjects. Linked independent component analysis (LICA) and correlation analysis were applied to identify associations between local neuroimaging patterns across modalities (including gray matter volume, white matter integrity, and spontaneous functional activity) and clinical factors.

RESULTS

LICA identified eight multimodal neuroimaging patterns related to phenotypic variations, including three related to symptoms and diagnosis. One imaging pattern (IC9) that included both the amplitude of low-frequency fluctuation measure of spontaneous functional activity and white matter integrity measures correlated negatively with OCD diagnosis and diagnostic scales. Two imaging patterns (IC10 and IC27) correlated with compulsion symptoms: IC10 included primarily anatomical measures and IC27 included primarily functional measures. In addition, we identified imaging patterns associated with age, gender, and emotional expression across subjects.

CONCLUSIONS

We established that data fusion techniques can identify local multimodal neuroimaging patterns associated with OCD phenotypes. The results inform our understanding of the neurobiological underpinnings of compulsive behaviors and OCD diagnosis.

Autism Is Associated With Interindividual Variations of Gray and White Matter Morphology

BACKGROUND

Although many studies have explored atypicalities in gray matter (GM) and white matter (WM) morphology of autism, most of them relied on unimodal analyses that did not benefit from the likelihood that different imaging modalities may reflect common neurobiology. We aimed to establish brain patterns of modalities that differentiate between individuals with and without autism and explore associations between these brain patterns and clinical measures in the autism group.

METHODS

We studied 183 individuals with autism and 157 nonautistic individuals (age range, 6-30 years) in a large, deeply phenotyped autism dataset (EU-AIMS LEAP [European Autism Interventions-A Multicentre Study for Developing New Medications Longitudinal European Autism Project]). Linked independent component analysis was used to link all participants' GM volume and WM diffusion tensor images, and group comparisons of modality shared variances were examined. Subsequently, we performed univariate and multivariate brain-behavior correlation analyses to separately explore the relationships between brain patterns and clinical profiles.

RESULTS

One multimodal pattern was significantly related to autism. This pattern was primarily associated with GM volume in bilateral insula and frontal, precentral and postcentral, cingulate, and caudate areas and co-occurred with altered WM features in the superior longitudinal fasciculus. The brain-behavior correlation analyses showed a significant multivariate association primarily between brain patterns that involved variation of WM and symptoms of restricted and repetitive behavior in the autism group.

CONCLUSIONS

Our findings demonstrate the assets of integrated analyses of GM and WM alterations to study the brain mechanisms that underpin autism and show that the complex clinical autism phenotype can be interpreted by brain covariation patterns that are spread across the brain involving both cortical and subcortical areas.

White matter alterations in Attention-Deficit/Hyperactivity Disorder (ADHD): a systematic review of 129 diffusion imaging studies with meta-analysis

Aberrant anatomical brain connections in attention-deficit/hyperactivity disorder (ADHD) are reported inconsistently across diffusion weighted imaging (DWI) studies. Based on a pre-registered protocol (Prospero: CRD42021259192), we searched PubMed, Ovid, and Web of Knowledge until 26/03/2022 to conduct a systematic review of DWI studies. We performed a quality assessment based on imaging acquisition, preprocessing, and analysis. Using signed differential mapping, we meta-analyzed a subset of the retrieved studies amenable to quantitative evidence synthesis, i.e., tract-based spatial statistics (TBSS) studies, in individuals of any age and, separately, in children, adults, and high-quality datasets. Finally, we conducted meta-regressions to test the effect of age, sex, and medication-naïvety. We included 129 studies (6739 ADHD participants and 6476 controls), of which 25 TBSS studies provided peak coordinates for case-control differences in fractional anisotropy (FA)(32 datasets) and 18 in mean diffusivity (MD)(23 datasets). The systematic review highlighted white matter alterations (especially reduced FA) in projection, commissural and association pathways of individuals with ADHD, which were associated with symptom severity and cognitive deficits. The meta-analysis showed a consistent reduced FA in the splenium and body of the corpus callosum, extending to the cingulum. Lower FA was related to older age, and case-control differences did not survive in the pediatric meta-analysis. About 68% of studies were of low quality, mainly due to acquisitions with non-isotropic voxels or lack of motion correction; and the sensitivity analysis in high-quality datasets yielded no significant results. Findings suggest prominent alterations in posterior interhemispheric connections subserving cognitive and motor functions affected in ADHD, although these might be influenced by non-optimal acquisition parameters/preprocessing. Absence of findings in children may be related to the late development of callosal fibers, which may enhance case-control differences in adulthood. Clinicodemographic and methodological differences were major barriers to consistency and comparability among studies, and should be addressed in future investigations.

Two neurosubtypes of ADHD different from the clinical phenotypes

Clinical and etiological variability of attention deficit hyperactivity disorder (ADHD) presents an obstacle to understand the disorder. The aim of this study was to disentangle the heterogeneity of ADHD using neuroimaging and a semi-supervised machine learning algorithm. We collected brain structural and functional magnetic resonance imaging (MRI) data and clinical profiles of 183 children with ADHD and 396 neurotypical controls from 7 independent sites. We also used an external validation set with 750 subjects. We adopted a semi-supervised clustering method to subtype ADHD by regional volumetric measures of gray matter, white matter, and fractional amplitude of low frequency fluctuation (fALFF). In addition, split sample test, leave-one-site-out test and external validation were applied to evaluate the reproducibility and stability of ADHD subtypes. Two stable and reproducible neurosubtypes of ADHD were disclosed, which were proved by the split-sample test and leave-one-site-out validation. The structural and functional patterns of ADHD subtypes were also stable in the external validation set. The current two neurosubtypes differed in clinical manifestations and volumetric gray matter, white matter volume and fALFF patterns. The current neurosubtypes of ADHD which were different from clinical phenotypes could facilitate understanding the underlying neuropathological and neurobiological mechanism of the disorder.

Linking functional and structural brain organisation with behaviour in autism: a multimodal EU-AIMS Longitudinal European Autism Project (LEAP) study

Neuroimaging analyses of brain structure and function in autism have typically been conducted in isolation, missing the sensitivity gains of linking data across modalities. Here we focus on the integration of structural and functional organisational properties of brain regions. We aim to identify novel brain-organisation phenotypes of autism. We utilised multimodal MRI (T1-, diffusion-weighted and resting state functional), behavioural and clinical data from the EU AIMS Longitudinal European Autism Project (LEAP) from autistic (n = 206) and non-autistic (n = 196) participants. Of these, 97 had data from 2 timepoints resulting in a total scan number of 466. Grey matter density maps, probabilistic tractography connectivity matrices and connectopic maps were extracted from respective MRI modalities and were then integrated with Linked Independent Component Analysis. Linear mixed-effects models were used to evaluate the relationship between components and group while accounting for covariates and non-independence of participants with longitudinal data. Additional models were run to investigate associations with dimensional measures of behaviour. We identified one component that differed significantly between groups (coefficient = 0.33, padj = 0.02). This was driven (99%) by variance of the right fusiform gyrus connectopic map 2. While there were multiple nominal (uncorrected p < 0.05) associations with behavioural measures, none were significant following multiple comparison correction. Our analysis considered the relative contributions of both structural and functional brain phenotypes simultaneously, finding that functional phenotypes drive associations with autism. These findings expanded on previous unimodal studies by revealing the topographic organisation of functional connectivity patterns specific to autism and warrant further investigation.

Examining post-concussion white matter change in a pediatric sample

Diffusion-Weight Imaging (DWI) is increasingly used to explore a range of outcomes in pediatric concussion, particularly the neurobiological underpinnings of symptom recovery. However, the DWI findings within the broader pediatric concussion literature are mixed, which can largely be explained by methodological heterogeneity. To address some of these limitations, the aim of the present study was to utilize internationally- recognized criteria for concussion and a consistent imaging timepoint to conduct a comprehensive, multi-parametric survey of white matter microstructure after concussion. Forty-three children presenting with concussion to the emergency department of a tertiary level pediatric hospital underwent neuroimaging and were classified as either normally recovering (n = 27), or delayed recovering (n = 14) based on their post-concussion symptoms at 2 weeks post-injury.We combined multiple DWI metrics across four modeling approaches using Linked Independent Component Analysis (LICA) to extract several independent patterns of covariation in tissue microstructure present in the study cohort. Our analysis did not identify significant differences between the symptomatic and asymptomatic groups and no component significantly predicted delayed recovery. If white matter microstructure changes are implicated in delayed recovery from concussion, these findings, alongside previous work, suggest that current diffusion techniques are insufficient to detect those changes at this time.

Association of neurostructural biomarkers with secondary attention-deficit/hyperactivity disorder (ADHD) symptom severity in children with traumatic brain injury: a prospective cohort study

BACKGROUND

Despite a well-established link between childhood traumatic brain injury (TBI) and elevated secondary attention-deficit/hyperactivity disorder (s-ADHD) symptomology, the neurostructural correlates of these symptoms are largely unknown. Based on the influential 'triple-network model' of ADHD, this prospective longitudinal investigation aimed to (i) assess the effect of childhood TBI on brain morphometry of higher-order cognitive networks proposed to play a key role in ADHD pathophysiology, including the default-mode network (DMN), salience network (SN) and central executive network (CEN); and (ii) assess the independent prognostic value of DMN, SN and CEN morphometry in predicting s-ADHD symptom severity after childhood TBI.

METHODS

The study sample comprised 155 participants, including 112 children with medically confirmed mild-severe TBI ascertained from consecutive hospital admissions, and 43 typically developing (TD) children matched for age, sex and socio-economic status. High-resolution structural brain magnetic resonance imaging (MRI) sequences were acquired sub-acutely in a subset of 103 children with TBI and 34 TD children. Parents completed well-validated measures of ADHD symptom severity at 12-months post injury.

RESULTS

Relative to TD children and those with milder levels of TBI severity (mild, complicated mild, moderate), children with severe TBI showed altered brain morphometry within large-scale, higher-order cognitive networks, including significantly diminished grey matter volumes within the DMN, SN and CEN. When compared with the TD group, the TBI group showed significantly higher ADHD symptomatology and higher rates of clinically elevated symptoms. In multivariable models adjusted for other well-established risk factors, altered DMN morphometry independently predicted higher s-ADHD symptomatology at 12-months post-injury, whilst SN and CEN morphometry were not significant independent predictors.

CONCLUSIONS

Our prospective study findings suggest that neurostructural alterations within higher-order cognitive circuitry may represent a prospective risk factor for s-ADHD symptomatology at 12-months post-injury in children with TBI. High-resolution structural brain MRI has potential to provide early prognostic biomarkers that may help early identification of high-risk children with TBI who are likely to benefit from early surveillance and preventive measures to optimise long-term neuropsychiatric outcomes.

Male-specific, replicable and functional roles of genetic variants and cerebral gray matter volumes in ADHD: a gene-wide association study across KTN1 and a region-wide functional validation across brain

Attention deficit hyperactivity disorder (ADHD) is associated with reduction of cortical and subcortical gray matter volumes (GMVs). The kinectin 1 gene (KTN1) has recently been reported to significantly regulate GMVs and ADHD risk. In this study, we aimed to identify sex-specific, replicable risk KTN1 alleles for ADHD and to explore their regulatory effects on mRNA expression and cortical and subcortical GMVs. We examined a total of 1020 KTN1 SNPs in one discovery sample (ABCD cohort: 5573 males and 5082 females) and three independent replication European samples (Samples #1 and #2 each with 802/122 and 472/141 male/female offspring with ADHD; and Sample #3 with 14,154/4945 ADHD and 17,948/16,246 healthy males/females) to identify replicable associations within each sex. We examined the regulatory effects of ADHD-risk alleles on the KTN1 mRNA expression in two European brain cohorts (n = 348), total intracranial volume (TIV) in 46 European cohorts (n = 18,713) and the ABCD cohort, as well as the GMVs of seven subcortical structures in 50 European cohorts (n = 38,258) and of 118 cortical and subcortical regions in the ABCD cohort. We found that four KTN1 variants significantly regulated the risk of ADHD with the same direction of effect in males across discovery and replication samples (0.003 ≤ p ≤ 0.041), but none in females. All four ADHD-risk alleles significantly decreased KTN1 mRNA expression in all brain regions examined (1.2 × 10^-5 ≤ p ≤ 0.039). The ADHD-risk alleles significantly increased basal ganglia (2.8 × 10^-22 ≤ p ≤ 0.040) and hippocampus (p = 0.010) GMVs but reduced amygdala GMV (p = 0.030) and TIV (0.010 < p ≤ 0.013). The ADHD-risk alleles also significantly reduced some cortical (right superior temporal pole, right rectus) and cerebellar but increased other cortical (0.007 ≤ p ≤ 0.050) GMVs. To conclude, we identified a set of replicable and functional risk KTN1 alleles for ADHD, specifically in males. KTN1 may play a critical role in the pathogenesis of ADHD, and the reduction of specific cortical and subcortical, including amygdalar but not basal ganglia or hippocampal, GMVs may serve as a neural marker of the genetic effects.

Multimodal fusion analysis of functional, cerebrovascular and structural neuroimaging in healthy aging subjects

Brain aging is a complex process that requires a multimodal approach. Neuroimaging can provide insights into brain morphology, functional organization, and vascular dynamics. However, most neuroimaging studies of aging have focused on each imaging modality separately, limiting the understanding of interrelations between processes identified by different modalities and their relevance to cognitive decline in aging. Here, we used a data-driven multimodal approach, linked independent component analysis (ICA), to jointly analyze magnetic resonance imaging (MRI) of grey matter volume, cerebrovascular, and functional network topographies in relation to measures of fluid intelligence. Neuroimaging and cognitive data from the Cambridge Centre for Ageing and Neuroscience study were used, with healthy participants aged 18-88 years (main dataset n = 215 and secondary dataset n = 433). Using linked ICA, functional network activities were characterized in independent components but not captured in the same component as structural and cerebrovascular patterns. Split-sample (n = 108/107) and out-of-sample (n = 433) validation analyses using linked ICA were also performed. Global grey matter volume with regional cerebrovascular changes and the right frontoparietal network activity were correlated with age-related and individual differences in fluid intelligence. This study presents the insights from linked ICA to bring together measurements from multiple imaging modalities, with independent and additive information. We propose that integrating multiple neuroimaging modalities allows better characterization of brain pattern variability and changes associated with healthy aging.

Association between attention-deficit/hyperactivity disorder symptom severity and white matter integrity moderated by in-scanner head motion

Attention-deficit/hyperactivity disorder (ADHD) is a common and debilitating neurodevelopmental disorder associated with various negative life impacts. The manifestation of ADHD is very heterogeneous, and previous investigations on neuroanatomical alterations in ADHD have yielded inconsistent results. We investigated the mediating effect of in-scanner head motion and ADHD hyperactivity severity on motion-corrected fractional anisotropy (FA) using diffusion tensor imaging in the currently largest sample (n = 739) of medication-naïve children and adolescents (age range 5-22 years). We used automated tractography to examine whole-brain and mean FA of the tracts most frequently reported in ADHD; corpus callosum forceps major and forceps minor, left and right superior-longitudinal fasciculus, and left and right corticospinal tract (CST). Associations between FA and hyperactivity severity appeared when in-scanner head motion was not accounted for as mediator. However, causal mediation analysis revealed that these effects are fully mediated through in-scanner head motion for whole-brain FA, the corpus callosum forceps minor, and left superior-longitudinal fasciculus. Direct effect of hyperactivity severity on FA was only found for the left CST. This study illustrates the crucial role of in-scanner head motion in the identification of white matter integrity alterations in ADHD and shows how neglecting irremediable motion artifacts causes spurious findings. When the mediating effect of in-scanner head motion on FA is accounted for, an association between hyperactivity severity and FA is only present for the left CST; this may play a crucial role in the manifestation of hyperactivity and impulsivity symptoms in ADHD.

White Matter Microstructure in Attention-Deficit/Hyperactivity Disorder: A Systematic Tractography Study in 654 Individuals

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by age-inappropriate levels of inattention and/or hyperactivity-impulsivity. ADHD has been related to differences in white matter (WM) microstructure. However, much remains unclear regarding the nature of these WM differences and which clinical aspects of ADHD they reflect. We systematically investigated whether fractional anisotropy (FA) is associated with current and/or lifetime categorical diagnosis, impairment in daily life, and continuous ADHD symptom measures.

METHODS

Diffusion-weighted imaging data were obtained from 654 participants (322 unaffected, 258 affected, 74 subthreshold; 7-29 years of age). We applied automated global probabilistic tractography on 18 major WM pathways. Linear mixed-effects regression models were used to examine associations of clinical measures with overall brain and tract-specific FA.

RESULTS

There were significant interactions of tract with all ADHD variables on FA. There were no significant associations of FA with current or lifetime diagnosis, nor with impairment. Lower FA in the right cingulum angular bundle was associated with higher hyperactivity-impulsivity symptom severity (p_{familywise error} = .045). There were no significant effects for other tracts.

CONCLUSIONS

This is the first time global probabilistic tractography has been applied to an ADHD dataset of this size. We found no evidence for altered FA in association with ADHD diagnosis. Our findings indicate that associations of FA with ADHD are not uniformly distributed across WM tracts. Continuous symptom measures of ADHD may be more sensitive to FA than diagnostic categories. The right cingulum angular bundle in particular may play a role in symptoms of hyperactivity and impulsivity.

Brain Volumetric Measurements in Children With Attention Deficit Hyperactivity Disorder: A Comparative Study Between Synthetic and Conventional Magnetic Resonance Imaging

Objective: To investigate the profiles of brain volumetric measurements in children with attention deficit hyperactivity disorder (ADHD), and the consistency of these brain volumetric measurements derived from the synthetic and conventional T1 weighted MRI (SyMRI and cT1w MRI).

Methods: Brain SyMRI and cT1w images were prospectively collected for 38 pediatric patients with ADHD and 38 healthy children (HC) with an age range of 6–14 years. The gray matter volume (GMV), white matter volume (WMV), cerebrospinal fluid (CSF), non-WM/GM/CSF (NoN), myelin, myelin fraction (MYF), brain parenchyma volume (BPV), and intracranial volume (ICV) were automatically estimated from SyMRI data, and the four matching measurements (GMV, WMV, BPV, ICV) were extracted from cT1w images. The group differences of brain volumetric measurements were performed, respectively, using analysis of covariance. Pearson correlation analysis and interclass correlation coefficient (ICC) were applied to evaluate the association between synthetic and cT1w MRI-derived measurements.

Results: As for the brain volumetric measurements extracted from SyMRI, significantly decreased GMV, WMV, BPV, and increased NON volume (p < 0.05) were found in the ADHD group compared with HC; No group differences were found in ICV, CSF, myelin volume and MYF (p > 0.05). With regard to GMV, WMV, BPV, and ICV estimated from cT1w images, the group differences between ADHD and HC were consistent with the results estimated from SyMRI. And these four measurements showed noticeable correlation between the two approaches (r = 0.692, 0.643, 0.898, 0.789, respectively, p < 0.001; ICC values are 0.809, 0.782, 0.946, 0.873, respectively).

Conclusion: Our study demonstrated a global brain development disability, but normal whole-brain myelination in children with ADHD. Moreover, our results demonstrated the high consistency of brain volumetric indices between synthetic and cT1w MRI in children, which indicates the high reliability of SyMRI in the child-brain volumetric analysis.

No support for white matter connectivity differences in the combined and inattentive ADHD presentations

Evidence from functional neuroimaging studies support neural differences between the Attention Deficit Hyperactivity Disorder (ADHD) presentation types. It remains unclear if these neural deficits also manifest at the structural level. We have previously shown that the ADHD combined, and ADHD inattentive types demonstrate differences in graph properties of structural covariance suggesting an underlying difference in neuroanatomical organization. The goal of this study was to examine and validate white matter brain organization between the two subtypes using both scalar and connectivity measures of brain white matter. We used both tract-based spatial statistical (TBSS) and tractography analyses with network-based Statistics (NBS) and graph-theoretical analyses in a cohort of 35 ADHD participants (aged 8–17 years) defined using DSM-IV criteria as combined (ADHD-C) type (n = 19) or as predominantly inattentive (ADHD-I) type (n = 16), and 28 matched neurotypical controls. We performed TBSS analyses on scalar measures of fractional anisotropy (FA), mean (MD), radial (RD), and axial (AD) diffusivity to assess differences in WM between ADHD types and controls. NBS and graph theoretical analysis of whole brain inter-regional tractography examined connectomic differences and brain network organization, respectively. None of the scalar measures significantly differed between ADHD types or relative to controls. Similarly, there were no tractography connectivity differences between the two subtypes and relative to controls using NBS. Global and regional graph measures were also similar between the groups. A single significant finding was observed for nodal degree between the ADHD-C and controls, in the right insula (corrected p = .029). Our result of no white matter differences between the subtypes is consistent with most previous findings. These findings together might suggest that the white matter structural architecture is largely similar between the DSM-based ADHD presentations is similar to the extent of being undetectable with the current cohort size.

Lenticular nucleus volume predicts performance in real-time strategy game: cross-sectional and training approach using voxel-based morphometry

It is unclear why some people learn faster than others. We performed two independent studies in which we investigated the neural basis of real-time strategy (RTS) gaming and neural predictors of RTS game skill acquisition. In the first (cross-sectional) study, we found that experts in the RTS game StarCraft® II (SC2) had a larger lenticular nucleus volume (LNV) than non-RTS players. We followed a cross-validation procedure where we used the volume of regions identified in the first study to predict the quality of learning a new, complex skill (SC2) in a sample of individuals who were naive to RTS games (a second (training) study). Our findings provide new insights into how the LNV, which is associated with motor as well as cognitive functions, can be utilized to predict successful skill learning and be applied to a much broader context than just video games, such as contributing to optimizing cognitive training interventions.

Uncovering neurodevelopmental paths to autism spectrum disorder through an integrated analysis of developmental measures and neural sensitivity to faces

BACKGROUND

Autism spectrum disorder (ASD) is highly heterogeneous in its etiology and manifestation. The neurobiological processes underlying ASD development are reflected in multiple features, from behaviour and cognition to brain functioning. An integrated analysis of these features may optimize the identification of these processes.

METHODS

We examined cognitive and adaptive functioning and ASD symptoms between 8 and 36 months in 161 infants at familial high risk for ASD and 71 low-risk controls; we also examined neural sensitivity to eye gaze at 8 months in a subsample of 140 high-risk and 61 low-risk infants. We used linked independent component analysis to extract patterns of variation across domains and development, and we selected the patterns significantly associated with clinical classification at 36 months.

RESULTS

An early process at 8 months, indicating high levels of functioning and low levels of symptoms linked to higher attention to gaze shifts, was reduced in infants who developed ASD. A longitudinal process of increasing functioning and low levels of symptoms was reduced in infants who developed ASD, and another process suggesting a stagnation in cognitive functioning at 24 months was increased in infants who developed ASD.

LIMITATIONS

Although the results showed a clear significant trend relating to clinical classification, we found substantial overlap between groups.

CONCLUSION

We uncovered underlying processes that acted together early in development and were associated with clinical outcomes. Our results highlighted the complexity of emerging ASD, which goes beyond the borders of clinical categories. Future work should integrate genetic data to investigate the specific genetic risks linked to these processes.

Reduced fronto-striatal volume in attention-deficit/hyperactivity disorder in two cohorts across the lifespan

Attention-Deficit/Hyperactivity Disorder (ADHD) has been associated with altered brain anatomy in neuroimaging studies. However, small and heterogeneous study samples, and the use of region-of-interest and tissue-specific analyses have limited the consistency and replicability of these effects. We used a data-driven multivariate approach to investigate neuroanatomical features associated with ADHD in two independent cohorts: the Dutch NeuroIMAGE cohort (n = 890, 17.2 years) and the Brazilian IMpACT cohort (n = 180, 44.2 years). Using independent component analysis of whole-brain morphometry images, 375 neuroanatomical components were assessed for association with ADHD. In both discovery (corrected-p = 0.0085) and replication (p = 0.032) cohorts, ADHD was associated with reduced volume in frontal lobes, striatum, and their interconnecting white-matter. Current results provide further evidence for the role of the fronto-striatal circuit in ADHD in children, and for the first time show its relevance to ADHD in adults. The fact that the cohorts are from different continents and comprise different age ranges highlights the robustness of the findings.

Structural brain alterations and their association with cognitive function and symptoms in Attention-deficit/Hyperactivity Disorder families

Gray matter disruptions have been found consistently in Attention-deficit/Hyperactivity Disorder (ADHD). The organization of these alterations into brain structural networks remains largely unexplored. We investigated 508 participants (281 males) with ADHD (N = 210), their unaffected siblings (N = 108), individuals with subthreshold ADHD (N = 49), and unrelated healthy controls (N = 141) with an age range from 7 to 18 years old from 336 families in the Dutch NeuroIMAGE project. Source based morphometry was used to examine structural brain network alterations and their association with symptoms and cognitive performance. Two networks showed significant reductions in individuals with ADHD compared to unrelated healthy controls after False Discovery Rate correction. Component A, mainly located in bilateral Crus I, showed a ADHD/typically developing difference with subthreshold cases being intermediate between ADHD and typically developing controls. The unaffected siblings were similar to controls. After correcting for IQ and medication status, component A showed a negative correlation with inattention symptoms across the entire sample. Component B included a maximum cluster in the bilateral insula, where unaffected siblings, similar to individuals with ADHD, showed significantly reduced loadings compared to controls; but no relationship with individual symptoms or cognitive measures was found for component B. This multivariate approach suggests that areas reflecting genetic liability within ADHD are partly separate from those areas modulating symptom severity.

Individual differences v. the average patient: mapping the heterogeneity in ADHD using normative models

BACKGROUND

The present paper presents a fundamentally novel approach to model individual differences of persons with the same biologically heterogeneous mental disorder. Unlike prevalent case-control analyses, that assume a clear distinction between patient and control groups and thereby introducing the concept of an 'average patient', we describe each patient's biology individually, gaining insights into the different facets that characterize persistent attention-deficit/hyperactivity disorder (ADHD).

METHODS

Using a normative modeling approach, we mapped inter-individual differences in reference to normative structural brain changes across the lifespan to examine the degree to which case-control analyses disguise differences between individuals.

RESULTS

At the level of the individual, deviations from the normative model were frequent in persistent ADHD. However, the overlap of more than 2% between participants with ADHD was only observed in few brain loci. On average, participants with ADHD showed significantly reduced gray matter in the cerebellum and hippocampus compared to healthy individuals. While the case-control differences were in line with the literature on ADHD, individuals with ADHD only marginally reflected these group differences.

CONCLUSIONS

Case-control comparisons, disguise inter-individual differences in brain biology in individuals with persistent ADHD. The present results show that the 'average ADHD patient' has limited informative value, providing the first evidence for the necessity to explore different biological facets of ADHD at the level of the individual and practical means to achieve this end.

Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis

Previous structural and functional neuroimaging studies have implicated distributed brain regions and networks in depression. However, there are no robust imaging biomarkers that are specific to depression, which may be due to clinical heterogeneity and neurobiological complexity. A dimensional approach and fusion of imaging modalities may yield a more coherent view of the neuronal correlates of depression. We used linked independent component analysis to fuse cortical macrostructure (thickness, area, gray matter density), white matter diffusion properties and resting-state functional magnetic resonance imaging default mode network amplitude in patients with a history of depression (n = 170) and controls (n = 71). We used univariate and machine learning approaches to assess the relationship between age, sex, case-control status, and symptom loads for depression and anxiety with the resulting brain components. Univariate analyses revealed strong associations between age and sex with mainly global but also regional specific brain components, with varying degrees of multimodal involvement. In contrast, there were no significant associations with case-control status, nor symptom loads for depression and anxiety with the brain components, nor any interaction effects with age and sex. Machine learning revealed low model performance for classifying patients from controls and predicting symptom loads for depression and anxiety, but high age prediction accuracy. Multimodal fusion of brain imaging data alone may not be sufficient for dissecting the clinical and neurobiological heterogeneity of depression. Precise clinical stratification and methods for brain phenotyping at the individual level based on large training samples may be needed to parse the neuroanatomy of depression.

Inter-individual differences in human brain structure and morphology link to variation in demographics and behavior

We perform a comprehensive integrative analysis of multiple structural MR-based brain features and find for the first-time strong evidence relating inter-individual brain structural variations to a wide range of demographic and behavioral variates across a large cohort of young healthy human volunteers. Our analyses reveal that a robust 'positive-negative' spectrum of behavioral and demographic variates, recently associated to covariation in brain function, can already be identified using only structural features, highlighting the importance of careful integration of structural features in any analysis of inter-individual differences in functional connectivity and downstream associations with behavioral/demographic variates.

Specific patterns of brain alterations underlie distinct clinical profiles in Huntington's disease

Huntington's disease (HD) is a genetic neurodegenerative disease which involves a triad of motor, cognitive and psychiatric disturbances. However, there is great variability in the prominence of each type of symptom across individuals. The neurobiological basis of such variability remains poorly understood but would be crucial for better tailored treatments. Multivariate multimodal neuroimaging approaches have been successful in disentangling these profiles in other disorders. Thus we applied for the first time such approach to HD. We studied the relationship between HD symptom domains and multimodal measures sensitive to grey and white matter structural alterations. Forty-three HD gene carriers (23 manifest and 20 premanifest individuals) were scanned and underwent behavioural assessments evaluating motor, cognitive and psychiatric domains. We conducted a multimodal analysis integrating different structural neuroimaging modalities measuring grey matter volume, cortical thickness and white matter diffusion indices - fractional anisotropy and radial diffusivity. All neuroimaging measures were entered into a linked independent component analysis in order to obtain multimodal components reflecting common inter-subject variation across imaging modalities. The relationship between multimodal neuroimaging independent components and behavioural measures was analysed using multiple linear regression. We found that cognitive and motor symptoms shared a common neurobiological basis, whereas the psychiatric domain presented a differentiated neural signature. Behavioural measures of different symptom domains correlated with different neuroimaging components, both the brain regions involved and the neuroimaging modalities most prominently associated with each type of symptom showing differences. More severe cognitive and motor signs together were associated with a multimodal component consisting in a pattern of reduced grey matter, cortical thickness and white matter integrity in cognitive and motor related networks. In contrast, depressive symptoms were associated with a component mainly characterised by reduced cortical thickness pattern in limbic and paralimbic regions. In conclusion, using a multivariate multimodal approach we were able to disentangle the neurobiological substrates of two distinct symptom profiles in HD: one characterised by cognitive and motor features dissociated from a psychiatric profile. These results open a new view on a disease classically considered as a uniform entity and initiates a new avenue for further research considering these qualitative individual differences.

Structural Variability in the Human Brain Reflects Fine-Grained Functional Architecture at the Population Level

Human brain structure topography is thought to be related in part to functional specialization. However, the extent of such relationships is unclear. Here, using a data-driven, multimodal approach for studying brain structure across the lifespan (N = 484, n = 260 females), we demonstrate that numerous structural networks, covering the entire brain, follow a functionally meaningful architecture. These gray matter networks (GMNs) emerge from the covariation of gray matter volume and cortical area at the population level. We further reveal fine-grained anatomical signatures of functional connectivity. For example, within the cerebellum, a structural separation emerges between lobules that are functionally connected to distinct, mainly sensorimotor, cognitive and limbic regions of the cerebral cortex and subcortex. Structural modes of variation also replicate the fine-grained functional architecture seen in eight well defined visual areas in both task and resting-state fMRI. Furthermore, our study shows a structural distinction corresponding to the established segregation between anterior and posterior default-mode networks (DMNs). These fine-grained GMNs further cluster together to form functionally meaningful larger-scale organization. In particular, we identify a structural architecture bringing together the functional posterior DMN and its anticorrelated counterpart. In summary, our results demonstrate that the relationship between structural and functional connectivity is fine-grained, widespread across the entire brain, and driven by covariation in cortical area, i.e. likely differences in shape, depth, or number of foldings. These results suggest that neurotrophic events occur during development to dictate that the size and folding pattern of distant, functionally connected brain regions should vary together across subjects.SIGNIFICANCE STATEMENT Questions about the relationship between structure and function in the human brain have engaged neuroscientists for centuries in a debate that continues to this day. Here, by investigating intersubject variation in brain structure across a large number of individuals, we reveal modes of structural variation that map onto fine-grained functional organization across the entire brain, and specifically in the cerebellum, visual areas, and default-mode network. This functionally meaningful structural architecture emerges from the covariation of gray matter volume and cortical folding. These results suggest that the neurotrophic events at play during development, and possibly evolution, which dictate that the size and folding pattern of distant brain regions should vary together across subjects, might also play a role in functional cortical specialization.

Linked anatomical and functional brain alterations in children with attention-deficit/hyperactivity disorder

Objectives

Neuroimaging studies have independently demonstrated brain anatomical and functional impairments in participants with ADHD. The aim of the current study was to explore the relationship between structural and functional brain alterations in ADHD through an integrated analysis of multimodal neuroimaging data.

Methods

We performed a multimodal analysis to integrate resting-state functional magnetic resonance imaging (MRI), structural MRI, and diffusion-weighted imaging data in a large, single-site sample of children with and without diagnosis for ADHD. The inferred subject contributions were fed into regression models to investigate the relationships between diagnosis, symptom severity, gender, and age.

Results

Compared with controls, children with ADHD diagnosis showed altered white matter microstructure in widespread white matter fiber tracts as well as greater gray matter volume (GMV) in bilateral frontal regions, smaller GMV in posterior regions, and altered functional connectivity (FC) in default mode and fronto-parietal networks. Age-related growth of GMV of bilateral occipital lobe, FC in frontal regions as well as age-related decline of GMV in medial regions seen in controls appeared reversed in children with ADHD. In the whole group, higher symptom severity was related to smaller GMV in widespread regions in bilateral frontal, parietal, and temporal lobes, as well as greater GMV in intracalcarine and temporal cortices.

Conclusions

Through a multimodal analysis approach we show that structural and functional alterations in brain regions known to be altered in subjects with ADHD from unimodal studies are linked across modalities. The brain alterations were related to clinical features of ADHD, including disorder status, age, and symptom severity.

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Multimodal imaging analysis provides new insights in interconnected imaging findings.

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Co-occurrence of structural and functional brain alterations were observed in ADHD.

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The identified multimodal alterations are related to clinical features of ADHD.

A Review of Heterogeneity in Attention Deficit/Hyperactivity Disorder (ADHD)

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that affects approximately 8%–12% of children worldwide. Throughout an individual’s lifetime, ADHD can significantly increase risk for other psychiatric disorders, educational and occupational failure, accidents, criminality, social disability and addictions. No single risk factor is necessary or sufficient to cause ADHD. The multifactorial causation of ADHD is reflected in the heterogeneity of this disorder, as indicated by its diversity of psychiatric comorbidities, varied clinical profiles, patterns of neurocognitive impairment and developmental trajectories, and the wide range of structural and functional brain anomalies. Although evidence-based treatments can reduce ADHD symptoms in a substantial portion of affected individuals, there is yet no curative treatment for ADHD. A number of theoretical models of the emergence and developmental trajectories of ADHD have been proposed, aimed at providing systematic guides for clinical research and practice. We conducted a comprehensive review of the current status of research in understanding the heterogeneity of ADHD in terms of etiology, clinical profiles and trajectories, and neurobiological mechanisms. We suggest that further research focus on investigating the impact of the etiological risk factors and their interactions with developmental neural mechanisms and clinical profiles in ADHD. Such research would have heuristic value for identifying biologically homogeneous subgroups and could facilitate the development of novel and more tailored interventions that target underlying neural anomalies characteristic of more homogeneous subgroups.

Cognitive Working Memory Training (CWMT) in adolescents suffering from Attention-Deficit/Hyperactivity Disorder (ADHD): A controlled trial taking into account concomitant medication effects

Although, cognitive working memory training (CWMT) has been reported to enhance working memory functioning in youths with attention-deficit/ hyperactivity disorder (ADHD), few studies take into account the concomitant effects of medication. Sixty adolescents aged from 11 to 15 years were randomly assigned to CWMT treatment, whereas medication was either continued or not introduced (no randomization performed). Results revealed beneficial effects of CWMT on the different components of working memory (WM), namely the phonological loop, the visuospatial sketchpad and the central executive. In particular, CWMT allowed participants to obtain a level of performance similar to the typically-developing adolescents for the phonological loop (i.e., forward digit span) as well as for the visuospatial sketchpad (i.e., board span). For the central executive (i.e., backward digit span) the concomitant effects of CWMT and medication allows participants to obtain the performance level of the typically-developing adolescents. Although, no transfers were observed with respect to other cognitive functions, in medicated patients with ADHD, CWMT reduced hyperactivity / impulsivity symptoms at 2-month follow-up. The present study gives evidence of the efficacy of CWMT to enhance WM performance, as well as, to reduce symptoms. The overall results highlight the usefulness of multimodal interventions.

Prenatal stress and the developing brain: Risks for neurodevelopmental disorders

The prenatal period is increasingly considered as a crucial target for the primary prevention of neurodevelopmental and psychiatric disorders. Understanding their pathophysiological mechanisms remains a great challenge. Our review reveals new insights from prenatal brain development research, involving (epi)genetic research, neuroscience, recent imaging techniques, physical modeling, and computational simulation studies. Studies examining the effect of prenatal exposure to maternal distress on offspring brain development, using brain imaging techniques, reveal effects at birth and up into adulthood. Structural and functional changes are observed in several brain regions including the prefrontal, parietal, and temporal lobes, as well as the cerebellum, hippocampus, and amygdala. Furthermore, alterations are seen in functional connectivity of amygdalar-thalamus networks and in intrinsic brain networks, including default mode and attentional networks. The observed changes underlie offspring behavioral, cognitive, emotional development, and susceptibility to neurodevelopmental and psychiatric disorders. It is concluded that used brain measures have not yet been validated with regard to sensitivity, specificity, accuracy, or robustness in predicting neurodevelopmental and psychiatric disorders. Therefore, more prospective long-term longitudinal follow-up studies starting early in pregnancy should be carried out, in order to examine brain developmental measures as mediators in mediating the link between prenatal stress and offspring behavioral, cognitive, and emotional problems and susceptibility for disorders.

Paediatric population neuroimaging and the Generation R Study: the second wave

Paediatric population neuroimaging is an emerging field that falls at the intersection between developmental neuroscience and epidemiology. A key feature of population neuroimaging studies involves large-scale recruitment that is representative of the general population. One successful approach for population neuroimaging is to embed neuroimaging studies within large epidemiological cohorts. The Generation R Study is a large, prospective population-based birth-cohort in which nearly 10,000 pregnant mothers were recruited between 2002 and 2006 with repeated measurements in the children and their parents over time. Magnetic resonance imaging was included in 2009 with the scanning of 1070 6-to-9-year-old children. The second neuroimaging wave was initiated in April 2013 with a total of 4245 visiting the MRI suite and 4087 9-to-11-year-old children being scanned. The sequences included high-resolution structural MRI, 35-direction diffusion weighted imaging, and a 6 min and 2 s resting-state functional MRI scan. The goal of this paper is to provide an overview of the imaging protocol and the overlap between the neuroimaging data and metadata. We conclude by providing a brief overview of results from our first wave of neuroimaging, which highlights a diverse array of questions that can be addressed by merging the fields of developmental neuroscience and epidemiology.

Refinement by integration: aggregated effects of multimodal imaging markers on adult ADHD

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) is biologically heterogeneous, with different biological predispositions - mediated through developmental processes - converging upon a common clinical phenotype. Brain imaging studies have variably shown altered brain structure, activity and connectivity in children and adults with ADHD. Recent methodological developments allow for the integration of information across imaging modalities, potentially yielding a more coherent view regarding the biology underlying the disorder.

METHODS

We analyzed a sample of adults with persistent ADHD and healthy controls using an advanced multimodal linked independent component analysis approach. Diffusion and structural MRI data were fused to form imaging markers reflecting independent components that explain variation across modalities. We included these markers as predictors into logistic regression models on adult ADHD and put those into context with predictions of estimated intelligence, age and sex.

RESULTS

We included 87 adults with ADHD and 93 controls in our analysis. Participants' courses associated with all imaging markers explained 27.86% of the variance in adult ADHD. No single imaging modality dominated this result. Instead, it was explained by aggregation of relatively small effects across several modalities and markers. One of the top markers for adult ADHD was multimodal and linked to morphological and microstructural effects within anterior temporal brain regions; another was linked to cortical thickness. Several markers were also influenced by estimated intelligence, age and/or sex.

LIMITATIONS

Although complex analytical approaches, such as the one applied here, provide insight into otherwise hidden mechanisms, they also increase the complexity of interpretations.

CONCLUSION

No dominant imaging modality or marker characterizes structural brain phenotypes in adults with ADHD, but we can refine our characterization of the disorder by the integration of small effects across modalities.

Distinguishing early and late brain aging from the Alzheimer's disease spectrum: consistent morphological patterns across independent samples

Alzheimer's disease (AD) is a debilitating age-related neurodegenerative disorder. Accurate identification of individuals at risk is complicated as AD shares cognitive and brain features with aging. We applied linked independent component analysis (LICA) on three complementary measures of gray matter structure: cortical thickness, area and gray matter density of 137 AD, 78 mild (MCI) and 38 subjective cognitive impairment patients, and 355 healthy adults aged 18-78 years to identify dissociable multivariate morphological patterns sensitive to age and diagnosis. Using the lasso classifier, we performed group classification and prediction of cognition and age at different age ranges to assess the sensitivity and diagnostic accuracy of the LICA patterns in relation to AD, as well as early and late healthy aging. Three components showed high sensitivity to the diagnosis and cognitive status of AD, with different relationships with age: one reflected an anterior-posterior gradient in thickness and gray matter density and was uniquely related to diagnosis, whereas the other two, reflecting widespread cortical thickness and medial temporal lobe volume, respectively, also correlated significantly with age. Repeating the LICA decomposition and between-subject analysis on ADNI data, including 186 AD, 395 MCI and 220 age-matched healthy controls, revealed largely consistent brain patterns and clinical associations across samples. Classification results showed that multivariate LICA-derived brain characteristics could be used to predict AD and age with high accuracy (area under ROC curve up to 0.93 for classification of AD from controls). Comparison between classifiers based on feature ranking and feature selection suggests both common and unique feature sets implicated in AD and aging, and provides evidence of distinct age-related differences in early compared to late aging.

Distinct multivariate brain morphological patterns and their added predictive value with cognitive and polygenic risk scores in mental disorders

The brain underpinnings of schizophrenia and bipolar disorders are multidimensional, reflecting complex pathological processes and causal pathways, requiring multivariate techniques to disentangle. Furthermore, little is known about the complementary clinical value of brain structural phenotypes when combined with data on cognitive performance and genetic risk. Using data-driven fusion of cortical thickness, surface area, and gray matter density maps (GMD), we found six biologically meaningful patterns showing strong group effects, including four statistically independent multimodal patterns reflecting co-occurring alterations in thickness and GMD in patients, over and above two other independent patterns of widespread thickness and area reduction. Case-control classification using cognitive scores alone revealed high accuracy, and adding imaging features or polygenic risk scores increased performance, suggesting their complementary predictive value with cognitive scores being the most sensitive features. Multivariate pattern analyses reveal distinct patterns of brain morphology in mental disorders, provide insights on the relative importance between brain structure, cognitive and polygenetic risk score in classification of patients, and demonstrate the importance of multivariate approaches in studying the pathophysiological substrate of these complex disorders.

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Linked ICA showed six independent multivariate morphology patterns sensitive to SZ.

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Machine learning used to compare brain structure, cognitive and genetic scores.

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Cognition showed highest prediction of SZ, boosted by brain structure or genetics.

Dissociable diffusion MRI patterns of white matter microstructure and connectivity in Alzheimer's disease spectrum

Recent efforts using diffusion tensor imaging (DTI) have documented white matter (WM) alterations in Alzheimer's disease (AD). The full potential of whole-brain DTI, however, has not been fully exploited as studies have focused on individual microstructural indices independently. In patients with AD (n = 79), mild (MCI, n = 55) and subjective (SCI, n = 30) cognitive impairment, we applied linked independent component analysis (LICA) to model inter-subject variability across five complementary DTI measures (fractional anisotropy (FA), axial/radial/mean diffusivity, diffusion tensor mode), two crossing fiber measures estimated using a multi-compartment crossing-fiber model reflecting the volume fraction of the dominant (f1) and non-dominant (f2) diffusion orientation, and finally, connectivity density obtained from full-brain probabilistic tractography. The LICA component explaining the largest data variance was highly sensitive to disease severity (AD < MCI < SCI) and revealed widespread coordinated decreases in FA and f1 with increases in all diffusivity measures in AD. Additionally, it reflected regional coordinated decreases and increases in f2, mode and connectivity density, implicating bidirectional alterations of crossing fibers in the fornix, uncinate fasciculi, corpus callosum and major sensorimotor pathways. LICA yielded improved diagnostic classification performance compared to univariate region-of-interest features. Our results document coordinated WM microstructural and connectivity alterations in line with disease severity across the AD continuum.

Structural brain imaging correlates of ASD and ADHD across the lifespan: a hypothesis-generating review on developmental ASD-ADHD subtypes

We hypothesize that it is plausible that biologically distinct developmental ASD-ADHD subtypes are present, each characterized by a distinct time of onset of symptoms, progression and combination of symptoms. The aim of the present narrative review was to explore if structural brain imaging studies may shed light on key brain areas that are linked to both ASD and ADHD symptoms and undergo significant changes during development. These findings may possibly pinpoint to brain mechanisms underlying differential developmental ASD-ADHD subtypes. To this end we brought together the literature on ASD and ADHD structural brain imaging symptoms and particularly highlight the adolescent years and beyond. Findings indicate that the vast majority of existing MRI studies has been cross-sectional and conducted in children, and sometimes did include adolescents as well, but without explicitly documenting on this age group. MRI studies documenting on age effects in adults with ASD and/or ADHD are rare, and if age is taken into account, only linear effects are examined. Data from various studies suggest that a crucial distinctive feature underlying different developmental ASD-ADHD subtypes may be the differential developmental thinning patterns of the anterior cingulate cortex and related connections towards other prefrontal regions. These regions are crucial for the development of cognitive/effortful control and socio-emotional functioning, with impairments in these features as key to both ASD and ADHD.