Gray Matter Patterns Research Articles

Strength of spatial correlation between gray matter connectivity and patterns of proto-oncogene and neural network construction gene expression is associated with diffuse glioma survival.

Like other forms of neuropathology, gliomas appear to spread along neural pathways. Accordingly, our group and others have previously shown that brain network connectivity is highly predictive of glioma survival. In this study, we aimed to examine the molecular mechanisms of this relationship via imaging transcriptomics. We retrospectively obtained presurgical, T1-weighted MRI datasets from 669 adult patients, newly diagnosed with diffuse glioma. We measured brain connectivity using gray matter networks and coregistered these data with a transcriptomic brain atlas to determine the spatial co-localization between brain connectivity and expression patterns for 14 proto-oncogenes and 3 neural network construction genes. We found that all 17 genes were significantly co-localized with brain connectivity (p < 0.03, corrected). The strength of co-localization was highly predictive of overall survival in a cross-validated Cox Proportional Hazards model (mean area under the curve, AUC = 0.68 +/- 0.01) and significantly (p < 0.001) more so for a random forest survival model (mean AUC = 0.97 +/- 0.06). Bayesian network analysis demonstrated direct and indirect causal relationships among gene-brain co-localizations and survival. Gene ontology analysis showed that metabolic processes were overexpressed when spatial co-localization between brain connectivity and gene transcription was highest (p < 0.001). Drug-gene interaction analysis identified 84 potential candidate therapies based on our findings. Our findings provide novel insights regarding how gene-brain connectivity interactions may affect glioma survival.

Structural Brain Correlates of Physical Activity in Offspring of Parents with and without Exceptional Longevity

AbstractBackgroundPrevious studies suggest that frequent participation in physical activities (PA) promotes healthy aging by decreasing late‐life morbidity. Several studies further suggest that the volume of the prefrontal cortex and the hippocampus increases in response to PA. Brain volume is also preserved among offspring of those with exceptional longevity. This research examined associations between self‐reported PA and gray matter volume in older adults without dementia, and as function of parental longevity.MethodsSelf‐reported PA (activity level (low/high); activity rating (low/high); city blocks walked (&lt;10/≥10); weekly aerobic exercise (&lt;4/≥4); flights of stairs (≤5/&gt;5); longest continuous walk (&lt;4/≥4) and structural MRI data from 107 Ashkenazi Jewish older adults (79.08±6.52 years; 57.94% women) was examined. 59.81% of participants were offspring of parents with exceptional longevity (at least one parent lived 95 years or more) and 40.19% were offspring of parents with usual survival (both parents lived less than 95 years). Voxel‐based morphometry and multivariate covariance‐based analyses – adjusted for age, sex, years of education, and total intracranial volume – were used to identify gray matter patterns associated with PA. Linear regression was used to determine if these patterns varied as a function of parental longevity.ResultsSignificant gray matter covariance patterns associated with PA ratings (p = 0.001) and flight of stairs (p = 0.004) were derived. The gray matter pattern associated with flight of stairs were primarily composed of middle cingulate, thalamus, superior frontal gyrus and insula. The gray matter pattern associated with PA level were primarily composed of caudate nucleus, insula, and hippocampus. None of the derived gray matter patterns varied as a function of parental longevity. No significant patterns were observed for city blocks walked, weekly exercise, or longest continuous walk.ConclusionOur results suggest that self‐reported PA – when assessed with activity rating and flight of stairs climbed – are associated with gray matter volume pattern composed of hippocampal, cingulate, insular, and prefrontal regions. Our results further demonstrated that these gray matter patterns do not vary as a function of parental longevity. Participating in PA may benefit the aging brain to a similar extent in offspring of parents with and without exceptional longevity.

Multi‐Modal Structural Covariance Patterns Predictive of Dementia Ratings and Geriatric Depression Severity

AbstractBackgroundMore than 6 million Americans suffer from Alzheimer’s disease (AD), the most common age‐associated, neurodegenerative dementia, characterized by progressive cognitive decline and the sporadic emergence of neuropsychiatric symptoms (NPS). We applied linked independent component analysis (LICA), a unsupervised learning approach for data fusion, to identify multi‐modal gray matter patterns of covariation that were predictive of dementia ratings and depression symptoms to understand the brain circuitry underlying both.MethodPublicly available data from 277 participants (177 healthy controls, 61 mild cognitive impairment, and 39 AD (aged 58 – 93) in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset were used to conduct secondary analyses.T1‐weighted MR images were used to compute spatial maps of cortical thickness (CT) and pial surface area (PSA) using Freesurfer and grey matter (GM) density maps using FSLVBM. CT, PSA and GM maps for each modality were concatenated across participants into 4D data files that were analyzed using FSL Linked ICA to estimate 25 multi‐modal spatial components and subject loadings quantifying the strength of each component pattern per participant. Random forest regression with 5‐fold cross validation was used to predict CDR and GDS from the resulting subject loadings (with age, sex and site).ResultTwo LICA components (Fig 1) had highest feature importance in predicting CDR (R2 = 0.41). The same two patterns (Fig1) plus two additional patterns (Fig 2) were most important in predicting GDS (R2 = 0.08).ConclusionGreater CDR was predicted by larger (Fig 1A) decreased GM and CT of widespread temporal lobe regions and (Fig 1B) of reduced GM of entorhinal cortex/mesial temporal lobe with nodes of the default mode network, implicated in disease progression. Greater GDS was also predicted by these two patterns as well as a pattern (Fig 2A) reflecting decreased GM in nucleus accumbens, amygdala and a cerebellar‐thalomocortical (CTC) circuit strongly implicated in cerebellar cognitive affective syndrome (CCAS) and another (Fig 2B) comprised of CT decreases in cortex and temporal regions and GM decreases in frontoparietal regions resembling areas of the frontopariatal functional connectivity network. These multi‐modal brain‐behavior mappings could be translated to better understand dementia and neuropsychiatric symptoms of AD.

Progressive reduction in basal ganglia explains and predicts cerebral structural alteration in type 2 diabetes

Type 2 diabetes is consistently reported to be associated with reduced gray matter, mainly in the cortical-striatal-limbic networks. However, little is known about how the progression of diabetes affects cerebral gray matter. To investigate, we collected 543 age- and sex-matched participants of nondiabetes, prediabetes, and diabetes. Voxel-based morphometry using a linear trend model was performed to reveal brain regions associated with disease progression. The Granger causal network of structural covariance was used to assess the causal relationships of brain structural alterations according to disease progression. Multivariate pattern analysis was applied for the stage-specific predictions of hyperglycemia. We detected a linear trend of gray matter volume reduction in the basal ganglia with disease progression (P < 0.05, FWER corrected), which caused a reduction in bilateral temporal gyri, frontal pole, parahippocampus, and bilateral posterior cingulate/precuneus volumes. In addition, the gray matter pattern of the basal ganglia could predict patients with diabetes (accuracy 60.12%, p = 0.002). In conclusion, the basal ganglia is the brain area with progressive gray matter reduction as diabetes progress. The reduced volume in the basal ganglia causes widespread gray matter reductions throughout diabetes progression. These findings indicate that the basal ganglia play a key role in diabetes by affecting the cortical-striatal-limbic network.

Deep learning for risk-based stratification of cognitively impaired individuals

Quantifying the risk of progression to Alzheimer's disease (AD) could help identify persons who could benefit from early interventions. We used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI, n= 544, discovery cohort) and the National Alzheimer's Coordinating Center (NACC, n= 508, validation cohort), subdividing individuals with mild cognitive impairment (MCI) into risk groups based on cerebrospinal fluid amyloid-β levels and identifying differential gray matter patterns. We then created models that fused neural networks with survival analysis, trained using non-parcellated T1-weighted brain MRIs from ADNI data, to predict the trajectories of MCI to AD conversion within the NACC cohort (integrated Brier score: 0.192 [discovery], and 0.108 [validation]). Using modern interpretability techniques, we verified that regions important for model prediction are classically associated with AD. We confirmed AD diagnosis labels using postmortem data. We conclude that our framework provides a strategy for risk-based stratification of individuals with MCI and for identifying regions key for disease prognosis.

Brain morphometric changes in fibromyalgia and the impact of psychometric and clinical factors: a volumetric and diffusion-tensor imaging study

BackgroundPrevious studies have repeatedly found distinct brain morphometric changes in patients with fibromyalgia (FM), mainly affecting gray and white matter abnormalities in areas related to sensory and affective pain processing. However, few studies have thus far linked different types of structural changes and not much is known about behavioral and clinical determinants that might influence the emergence and progression of such changes.MethodsWe used voxel-based morphometry (VBM) and diffusion-tensor imaging (DTI) to detect regional patterns of (micro)structural gray (GM) and white matter (WM) alterations in 23 patients with FM compared to 21 healthy controls (HC), while considering the influence of demographic, psychometric, and clinical variables (age, symptom severity, pain duration, heat pain threshold, depression scores).ResultsVBM and DTI revealed striking patterns of brain morphometric changes in FM patients. Bilateral middle temporal gyrus (MTG), parahippocampal gyrus, left dorsal anterior cingulate cortex (dACC), right putamen, right caudate nucleus, and left dorsolateral prefrontal cortex (DLPFC) showed significantly decreased GM volumes. In contrast, increased GM volume was observed in bilateral cerebellum and left thalamus. Beyond that, patients displayed microstructural changes of WM connectivity within the medial lemniscus, corpus callosum, and tracts surrounding and connecting the thalamus. Sensory-discriminative aspects of pain (pain severity, pain thresholds) primarily showed negative correlations with GM within bilateral putamen, pallidum, right midcingulate cortex (MCC), and multiple thalamic substructures, whereas the chronicity of pain was negatively correlated with GM volumes within right insular cortex and left rolandic operculum. Affective-motivational aspects of pain (depressive mood, general activity) were related to GM and FA values within bilateral putamen and thalamus.ConclusionsOur results suggest a variety of distinct structural brain changes in FM, particularly affecting areas involved in pain and emotion processing such as the thalamus, putamen, and insula.

Application of diffusion kurtosis imaging in neonatal brain development.

Deviations from the regular pattern of growth and development could lead to early childhood diseases, suggesting the importance of evaluating early brain development. Through this study, we aimed to explore the changing patterns of white matter and gray matter during neonatal brain development using diffusion kurtosis imaging (DKI). In total, 42 full-term neonates (within 28 days of birth) underwent conventional brain magnetic resonance imaging (MRI) and DKI. The DKI metrics (including kurtosis parameters and diffusion parameters) of white matter and deep gray matter were measured. DKI metrics from the different regions of interest (ROIs) were evaluated using the Kruskal-Wallis test and Bonferroni method. Spearman rank correlation analysis of the DKI metrics was conducted, and the age at the time of brain MRI acquisition was calculated. The subjects were divided into three groups according to their age at the time of brain MRI acquisition: the first group, neonates aged ≤7 days; the second group, neonates aged 8-14 days; and the third group, neonates aged 15-28 days. The rate of change in DKI metrics relative to the first group was computed. The mean kurtosis (MK), axial kurtosis (Ka), radial kurtosis (Kr), and fractional anisotropy (FA) values showed positive correlations, whereas mean diffusion (MD), axial diffusion (Da), and radial diffusion (Dr) values showed negative correlations with the age at the time of brain MRI acquisition. The absolute correlation coefficients between MK values of almost all ROIs (except genu of the corpus callosum and frontal white matter) and the age at the time of brain MRI acquisition were greater than other metrics. The kurtosis parameters and FA values of central white matter were significantly higher than that of peripheral white matter, whereas the MD and Dr values were significantly lower than that of peripheral white matter. The MK value of the posterior limb of the internal capsule was the highest among the white matter areas. The FA value of the splenium of the corpus callosum was significantly higher than that of the other white matter areas. The kurtosis parameters and FA values of globus pallidus and thalamus were significantly higher than those of the caudate nucleus and putamen, whereas the Da and Dr values of globus pallidus and thalamus were significantly lower than those of the caudate nucleus and putamen. The relative change rates of kurtosis parameters and FA values of all ROIs were greater than those of MD, Da, and Dr values. The amplitude of MK values of almost all ROIs (except for the genu of the corpus callosum and central white matter of the centrum semiovale level) was greater than that of other metrics. The relative change rates of the Kr values of most ROIs were greater than those of the Ka value, and the relative change rates of the Dr values of most ROIs were greater than those of the Da value. DKI parameters showed potential advantages in detecting the changes in brain microstructure during neonatal brain development.

The role of the cerebellum in sequencing and predicting social and non-social events in patients with bipolar disorder.

Advances in the operational mode of the cerebellum indicate a role in sequencing and predicting non-social and social events, crucial for individuals to optimize high-order functions, such as Theory of Mind (ToM). ToM deficits have been described in patients with remitted bipolar disorders (BD). The literature on BD patients' pathophysiology reports cerebellar alterations; however, sequential abilities have never been investigated and no study has previously focused on prediction abilities, which are needed to properly interpret events and to adapt to changes. To address this gap, we compared the performance of BD patients in the euthymic phase with healthy controls using two tests that require predictive processing: a ToM test that require implicit sequential processing and a test that explicitly assesses sequential abilities in non-ToM functions. Additionally, patterns of cerebellar gray matter (GM) alterations were compared between BD patients and controls using voxel-based morphometry. Impaired ToM and sequential skills were detected in BD patients, specifically when tasks required a greater predictive load. Behavioral performances might be consistent with patterns of GM reduction in cerebellar lobules Crus I-II, which are involved in advanced human functions. These results highlight the importance of deepening the cerebellar role in sequential and prediction abilities in patients with BD.

The Cerebellum Gets Social: Evidence from an Exploratory Study of Cerebellar, Neurodevelopmental, and Psychiatric Disorders.

Social prediction is a key feature of social cognition (SC), a function in which the modulating role of the cerebellum is recognized. Accordingly, cerebellar alterations are reported in cerebellar pathologies, neurodevelopmental disorders, and psychiatric conditions that show SC deficits. Nevertheless, to date, no study has directly compared populations representative of these three conditions with respect to SC and cerebellar alterations. Therefore, the present exploratory study aimed to compare the SC profiles of individuals with cerebellar neurodegenerative disorders (CB), autism (ASD), bipolar disorder type 2 (BD2), or healthy subjects (HS) using a battery of social tests requiring different degrees of prediction processing. The patterns of cerebellar gray matter (GM) alterations were compared among the groups using voxel-based morphometry. Compared to HS, the clinical groups showed common SC deficits in tasks involving a moderate to high level of prediction. The behavioral results of the clinical groups are consistent with the presence of overlapping GM reduction in cerebellar right Crus II, an area notably involved in complex social processing and prediction. Although exploratory and preliminary, these results deepen the cerebellar role in social prediction and highlight the transdiagnostic value of the cerebellum in social functioning and prediction in pathologies of different aetiologies, forecasting novel possibilities for shared interventions.

Atrophy in subcortical gray matter in adult patients with moyamoya disease

BackgroundAcute cerebrovascular accidents, long-term hypoperfusion, and/or remote neuronal degeneration may lead to structural alterations in patients with moyamoya disease (MMD). This study sought to comprehensively investigate the distribution characteristics of subcortical gray matter volume and their correlations with angiographic changes in the intracranial artery in patients with MMD. MethodOne hundred forty-two patients with MMD and 142 age- and sex-matched healthy controls underwent 3-dimensional high-resolution structural magnetic resonance imaging. Volumes of subcortical gray matter and subregions of the hippocampus and amygdala were calculated, and the degree of stenosis/occlusion of intracranial arteries in patients with MMD was evaluated on MR angiography.ResultsVolume reductions in the thalamus, caudate, putamen, hippocampus, amygdala, pallidum, and nucleus accumbens were found in patients with MMD. Hippocampal subfields and amygdala subnuclei in patients with MMD showed distinct vulnerability, and morphological alterations in specific subregions were more obvious than in the whole hippocampus/amygdala. Volume loss in several subcortical areas was related to disease duration and intracranial arterial changes.ConclusionsOur findings revealed structural alteration patterns of subcortical gray matter in MMD. The specific atrophy in subregions of the hippocampus and the amygdala suggested potential cognitive and affective impairments in MMD, which warrants further investigation. Chronic cerebral hemodynamic alterations in MMD may play a pivotal role in morphological changes in subcortical areas.

Decoding individual differences in expressing and suppressing anger from structural brain networks: A supervised machine learning approach

Anger can be broken down into different elements: a transitory state (state anger), a stable personality feature (trait anger), a tendency to express it (anger-out), or to suppress it (anger-in), and the ability to regulate it (anger control). These elements are characterized by individual differences that vary across a continuum. Among them, the abilities to express and suppress anger are of particular relevance as they determine outcomes and enable successful anger management in daily situations. The aim of this study was to demonstrate that anger suppression and expression can be decoded by patterns of grey matter of specific well-known brain networks. To this aim, a supervised machine learning technique, known as Kernel Ridge Regression, was used to predict anger expression and suppression scores of 212 healthy subjects from the grey matter concentration. Results show that individual differences in anger suppression were predicted by two grey matter patterns associated with the Default-Mode Network and the Salience Network. Additionally, individual differences in anger expression were predicted by a circuit mainly involving subcortical and fronto-temporal regions when considering whole brain grey matter features. These results expand previous findings regarding the neural bases of anger by showing that individual differences in specific anger-related components can be predicted by the grey matter features of specific networks.

Clinical and cortical similarities identified between bipolar disorder I and schizophrenia: A multivariate approach.

Structural neuroimaging studies have identified similarities in the brains of individuals diagnosed with schizophrenia (SZ) and bipolar I disorder (BP), with overlap in regions of gray matter (GM) deficits between the two disorders. Recent studies have also shown that the symptom phenotypes associated with SZ and BP may allow for a more precise categorization than the current diagnostic criteria. In this study, we sought to identify GM alterations that were unique to each disorder and whether those alterations were also related to unique symptom profiles. We analyzed the GM patterns and clinical symptom presentations using independent component analysis (ICA), hierarchical clustering, and n-way biclustering in a large (N ∼ 3,000), merged dataset of neuroimaging data from healthy volunteers (HV), and individuals with either SZ or BP. Component A showed a SZ and BP < HV GM pattern in the bilateral insula and cingulate gyrus. Component B showed a SZ and BP < HV GM pattern in the cerebellum and vermis. There were no significant differences between diagnostic groups in these components. Component C showed a SZ < HV and BP GM pattern bilaterally in the temporal poles. Hierarchical clustering of the PANSS scores and the ICA components did not yield new subgroups. N-way biclustering identified three unique subgroups of individuals within the sample that mapped onto different combinations of ICA components and symptom profiles categorized by the PANSS but no distinct diagnostic group differences. These multivariate results show that diagnostic boundaries are not clearly related to structural differences or distinct symptom profiles. Our findings add support that (1) BP tend to have less severe symptom profiles when compared to SZ on the PANSS without a clear distinction, and (2) all the gray matter alterations follow the pattern of SZ < BP < HV without a clear distinction between SZ and BP.

Individual prediction and classification of cognitive impairment in patients with white matter lesions based on gray matter volume.

BackgroundVascular risk factors like white matter lesions (WMLs) are increasingly recognized as risk factors for vascular dementia (VaD) and can predict Alzheimer’s disease (AD) at least a decade before the clinical stage of the disease. This study aimed to predict cognitive decline and use machine learning techniques to classify older individuals (aged 50 years or older) with WMLs as having vascular mild cognitive impairment (VaMCI), VaD, or in good cognitive health (CH).MethodsA total of 79 individuals with WMLs were selected for this study and categorized into the following 3 groups: CH (n=25), VaMCI (n=33), and VaD (n=21). Data from the entire cohort was then divided into a training dataset (n=56) and testing dataset (n=23). The data were extracted from gray matter (GM) segmentations using voxel-based morphometry (VBM). A relevance vector regression (RVR) approach was used to test the relationship between the structural brain images and clinical scores. To predict the individual-level subtypes, we applied 2 different machine learning-based classifiers: support vector machine (SVM) and Gaussian process classification (GPC). All predictive models were trained on the training dataset and then validated on the testing dataset of age-matched participants.ResultsMulti-domain cognitive performance could be predicted based on the pattern of GM atrophy in older people with WMLs using a RVR approach. The classification of VaD versus CH (cross-validation accuracy =93.94%, test set accuracy =76.92%) and VaMCI versus CH (cross-validation accuracy =95.24%, test set accuracy =87.50%) could be successfully achieved using both SVM and GPC. However, SVM (cross-validation accuracy =67.57%, test set accuracy =70.59%) performed better than GPC in the classification of VaD versus VaMCI.ConclusionsBased on the patterns of gray matter and RVR-based model could achieve prediction of cognitive test scores, and SVM and GPC could classify the severity of cognitive impairment in older people with WMLs.

Brain patterns of pace - but not rhythm - are associated with vascular disease in older adults.

Distinct domains of gait such as pace and rhythm are linked to an increased risk for cognitive decline, falls, and dementia in aging. The brain substrates supporting these domains and underlying diseases, however, remain relatively unknown. The current study aimed to identify patterns of gray matter volume (GMV) associated with pace and rhythm, and whether these patterns vary as a function of vascular and non-vascular comorbidities. A cross-sectional sample of 297 older adults (M Age=72.5 years ± 7.2 years, 43% women) without dementia was drawn from the Tasmanian Study of Cognition and Gait (TASCOG). Factor analyses were used to reduce eight quantitative gait variables into two domains. The "pace" domain was primarily composed of gait speed, stride length, and double support time. The "rhythm" domain was composed of swing time, stance time, and cadence. Multivariate covariance-based analyses adjusted for age, sex, education, total intracranial volume, and presence of mild cognitive impairment identified gray matter volume (GMV) patterns associated with pace and rhythm, as well as participant-specific expression (or factor) scores for each pattern. Pace was positively associated with GMV in the right superior temporal sulcus, bilateral supplementary motor areas (SMA), and bilateral cerebellar regions. Rhythm was positively associated with GMV in bilateral SMA, prefrontal, cingulate, and paracingulate cortices. The GMV pattern associated with pace was less expressed in participants with any vascular disease; this association was also found independently with hypertension, diabetes, and myocardial infarction. Both pace and rhythm domains of gait were associated with the volume of brain structures that have been linked to controlled and automatic aspects of gait control, as well as with structures involved in multisensory integration. Only the brain structures associated with pace, however, were associated with vascular disease.

Multivariate pattern analysis of brain structure predicts functional outcome after auditory-based cognitive training interventions

Cognitive gains following cognitive training interventions are associated with improved functioning in people with schizophrenia (SCZ). However, considerable inter-individual variability is observed. Here, we evaluate the sensitivity of brain structural features to predict functional response to auditory-based cognitive training (ABCT) at a single-subject level. We employed whole-brain multivariate pattern analysis with support vector machine (SVM) modeling to identify gray matter (GM) patterns that predicted higher vs. lower functioning after 40 h of ABCT at the single-subject level in SCZ patients. The generalization capacity of the SVM model was evaluated by applying the original model through an out-of-sample cross-validation analysis to unseen SCZ patients from an independent validation sample who underwent 50 h of ABCT. The whole-brain GM volume-based pattern classification predicted higher vs. lower functioning at follow-up with a balanced accuracy (BAC) of 69.4% (sensitivity 72.2%, specificity 66.7%) as determined by nested cross-validation. The neuroanatomical model was generalizable to an independent cohort with a BAC of 62.1% (sensitivity 90.9%, specificity 33.3%). In particular, greater baseline GM volumes in regions within superior temporal gyrus, thalamus, anterior cingulate, and cerebellum predicted improved functioning at the single-subject level following ABCT in SCZ participants. The present findings provide a structural MRI fingerprint associated with preserved GM volumes at a single baseline timepoint, which predicted improved functioning following an ABCT intervention, and serve as a model for how to facilitate precision clinical therapies for SCZ based on imaging data, operating at the single-subject level.

The Associations Between Grey Matter Volume Covariance Patterns and Gait Variability-The Tasmanian Study of Cognition and Gait.

Greater gait variability predicts dementia. However, little is known about the neural correlates of gait variability. The aims of this study were to determine (1) grey matter volume covariance patterns associated with gait variability and (2) whether these patterns were associated with specific cognitive domains. Participants (n = 351; mean age 71.9 ± 7.1) were randomly selected from the Southern Tasmanian electoral roll. Step time, step length, step width and double support time were measured using an electronic walkway. Gait variability was calculated as the standard deviation of all steps for each gait measure. Voxel-based morphometry and multivariate covariance-based analyses were used to identify grey matter patterns associated with each gait variability measure. The individual expressions of grey matter patterns were correlated with processing speed, memory, executive and visuospatial functions. The grey matter covariance pattern of double support time variability included frontal, medial temporal, anterior cingulate, insula, cerebellar and striatal regions. Greater expression of this pattern was correlated with poorer performance in all cognitive functions (p < 0.001). The covariance pattern of step length variability included frontal, temporal, insula, occipital and cerebellar regions and was correlated with all cognitive functions (p < 0.05), except memory (p = 0.76). The covariance pattern of step width variability was limited to the cerebellum and correlated only with memory (p = 0.047). No significant pattern was identified for step time variability. In conclusion, different grey matter covariance patterns were associated with individual gait variability measures. These patterns were also correlated with specific cognitive functions, suggesting common neural networks may underlie both gait and cognition.

Discrepancies of polygenic effects on symptom dimensions between adolescents and adults with ADHD

A significant proportion of individuals with attention-deficit/hyperactivity disorder (ADHD) show persistence into adulthood. The genetic and neural correlates of ADHD in adolescents versus adults remain poorly characterized. We investigated ADHD polygenic risk score (PRS) in relation to previously identified gray matter (GM) patterns, neurocognitive, and symptom findings in the same ADHD sample (462 adolescents & 422 adults from the NeuroIMAGE and IMpACT cohorts). Significant effects of ADHD PRS were found on hyperactivity and impulsivity symptoms in adolescents, hyperactivity symptom in adults, but not GM volume components. A distinct PRS effect between adolescents and adults on individual ADHD symptoms is suggested.

Homogeneous grey matter patterns in patients with obsessive-compulsive disorder

BackgroundChanges in grey matter volume have frequently been reported in patients with obsessive-compulsive disorder (OCD). Most studies performed whole brain or region-of-interest based analyses whereas grey matter volume based on structural covariance networks has barely been investigated up to now. Therefore, the present study investigated grey matter volume within structural covariance networks in a sample of 228 participants (n = 117 OCD patients, n = 111 healthy controls). MethodsFirst, an independent component analysis (ICA) was performed on all subjects’ preprocessed T1 images to derive covariance-dependent morphometric networks. Then, grey matter volume from each of the ICA-derived morphometric networks was extracted and compared between the groups. In addition, we performed logistic regressions and receiver operating characteristic (ROC) analyses to investigate whether network-related grey matter volume could serve as a characteristic that allows to differentiate patients from healthy volunteers. Moreover, we assessed grey matter pattern organization by correlating grey matter volume in all networks across all participants. Finally, we explored a potential association between grey matter volume or whole-brain grey matter pattern organization and clinical characteristics in terms of symptom severity and duration of illness. ResultsThere were only subtle group differences in network-related grey matter volume. Network-related grey matter volume had moreover a very poor discrimination performance. We found, however, significant group differences with regard to grey matter pattern organization. When correlating grey matter volume in all networks across all participants, patients showed a significantly higher homogeneity across all networks and a significantly lower heterogeneity, as assessed by the coefficient of variation across all networks as well as in several single networks. There was no association with clinical characteristics. ConclusionThe findings of the present study suggest that the pathological mechanisms of OCD reduce interindividual grey matter variability. We assume that common characteristics associated with the disorder may lead to a more uniform, disorder-specific morphometry.

Statistically Defined Parkinson's Disease Executive and Memory Cognitive Phenotypes: Demographic, Behavioral, and Structural Neuroimaging Comparisons.

Some individuals with Parkinson's disease (PD) experience working memory and inhibitory difficulties, others learning and memory difficulties, while some only minimal to no cognitive deficits for many years. To statistically derive PD executive and memory phenotypes, and compare PD phenotypes on disease and demographic variables, vascular risk factors, and specific neuroimaging variables with known associations to executive and memory function relative to non-PD peers. Non-demented individuals with PD (n = 116) and non-PD peers (n = 62) were recruited to complete neuropsychology measures, blood draw, and structural magnetic resonance imaging. Tests representing the cognitive domains of interest (4 executive function, 3 memory) were included in a k-means cluster analysis comprised of the PD participants. Resulting clusters were compared demographic and disease-related variables, vascular risk markers, gray/white regions of interest, and white matter connectivity between known regions involved in executive and memory functions (dorsolateral prefrontal cortices to caudate nuclei; entorhinal cortices to hippocampi). Clusters showed: 1) PD Executive, n = 25; 2) PD Memory, n = 35; 3) PD Cognitively Well; n = 56. Even after disease variable corrections, PD Executive had less subcortical gray matter, white matter, and fewer bilateral dorsolateral-prefrontal cortex to caudate nucleus connections; PD Memory showed bilaterally reduced entorhinal-hippocampal connections. PD Cognitively Well showed only reduced putamen volume and right entorhinal cortex to hippocampi connections relative to non-PD peers. Groups did not statistically differ on cortical integrity measures or cerebrovascular disease markers. PD cognitive phenotypes showed different structural gray and white matter patterns. We discuss data relative to phenotype demographics, cognitive patterns, and structural brain profiles.

Hierarchical cluster analysis of multimodal imaging data identifies brain atrophy and cognitive patterns in Parkinson’s disease

BackgroundParkinson's disease (PD) is a heterogeneous condition. Cluster analysis based on cortical thickness has been used to define distinct patterns of brain atrophy in PD. However, the potential of other neuroimaging modalities, such as white matter (WM) fractional anisotropy (FA), which has also been demonstrated to be altered in PD, has not been investigated. ObjectiveWe aim to characterize PD subtypes using a multimodal clustering approach based on cortical and subcortical gray matter (GM) volumes and FA measures. MethodsWe included T1-weighted and diffusion-weighted MRI data from 62 PD patients and 33 healthy controls. We extracted mean GM volumes from 48 cortical and 17 subcortical regions using FSL-VBM, and the mean FA from 20 WM tracts using Tract-Based Spatial Statistics (TBSS). Hierarchical cluster analysis was performed with the PD sample using Ward's linkage method. Whole-brain voxel-wise intergroup comparisons of VBM and TBSS data were also performed using FSL. Neuropsychological and demographic statistical analyses were conducted using IBM SPSS Statistics 25.0. ResultsWe identified three PD subtypes, with prominent differences in GM patterns and little WM involvement. One group (n = 15) with widespread cortical and subcortical GM volume and WM FA reductions and pronounced cognitive deficits; a second group (n = 21) with only cortical atrophy limited to frontal and temporal regions and more specific neuropsychological impairment, and a third group (n = 26) without detectable atrophy or cognition impairment. ConclusionMultimodal MRI data allows classifying PD patients into groups according to GM and WM patterns, which in turn are associated with the cognitive profile.