Considering total intracranial volume and other nuisance variables in brain voxel based morphometry in idiopathic PD

Genomic analysis of intracranial and subcortical brain volumes yields polygenic scores accounting for variation across ancestries

Subcortical brain structures are involved in developmental, psychiatric and neurological disorders. We performed GWAS meta-analyses of intracranial and nine subcortical brain volumes (brainstem, caudate nucleus, putamen, hippocampus, globus pallidus, thalamus, nucleus accumbens, amygdala and, for the first time, the ventral diencephalon) in 74,898 participants of European ancestry. We identified 254 independent loci associated with these brain volumes, explaining up to 35% of phenotypic variance. We observed gene expression in specific neural cell types across differentiation time points, including genes involved in intracellular signalling and brain ageing-related processes. Polygenic scores for brain volumes showed predictive ability when applied to individuals of diverse ancestries. We observed causal genetic effects of brain volumes with Parkinson's disease and ADHD. Findings implicate specific gene expression patterns in brain development and genetic variants in comorbid neuropsychiatric disorders, which could point to a brain substrate and region of action for risk genes implicated in brain diseases.

Larger putamen in individuals at risk and with manifest bipolar disorder

BACKGROUND

Individuals at risk for bipolar disorder (BD) have a wide range of genetic and non-genetic risk factors, like a positive family history of BD or (sub)threshold affective symptoms. Yet, it is unclear whether these individuals at risk and those diagnosed with BD share similar gray matter brain alterations.

METHODS

In 410 male and female participants aged 17-35 years, we compared gray matter volume (3T MRI) between individuals at risk for BD (as assessed using the EPIbipolar scale; n = 208), patients with a DSM-IV-TR diagnosis of BD (n = 87), and healthy controls (n = 115) using voxel-based morphometry in SPM12/CAT12. We applied conjunction analyses to identify similarities in gray matter volume alterations in individuals at risk and BD patients, relative to healthy controls. We also performed exploratory whole-brain analyses to identify differences in gray matter volume among groups. ComBat was used to harmonize imaging data from seven sites.

RESULTS

Both individuals at risk and BD patients showed larger volumes in the right putamen than healthy controls. Furthermore, individuals at risk had smaller volumes in the right inferior occipital gyrus, and BD patients had larger volumes in the left precuneus, compared to healthy controls. These findings were independent of course of illness (number of lifetime manic and depressive episodes, number of hospitalizations), comorbid diagnoses (major depressive disorder, attention-deficit hyperactivity disorder, anxiety disorder, eating disorder), familial risk, current disease severity (global functioning, remission status), and current medication intake.

CONCLUSIONS

Our findings indicate that alterations in the right putamen might constitute a vulnerability marker for BD.

Structural brain feature is associated with changes of masticatory performance in healthy elderly people: Evidence from longitudinal neuroimaging research over two years

BACKGROUND

Human neuroimaging studies have revealed the association between brain structure and masticatory function. However, the majority of the studies adopted a cross-sectional design, which hardly reveals the change in masticatory function and brain structure between different timepoints, and the dynamical association between changes in masticatory function and changes in brain structure has not been elucidated.

OBJECTIVE

With a longitudinal design, we assessed the association between changes in masticatory performance (MP) and regional brain volume.

METHODS

Twenty-two elderly participants received assessments of the number of missing teeth and MP (via colour-changeable chewing gum) when they entered the study (i.e. the initial stage, T0 ), approximately 6 months later (T0.5 ), and approximately 1-2 years later (T1 ). Difficulty of food intake was assessed using a questionnaire. The participants received magnetic resonance imaging (MRI) at T0 and T1 . The brain volume of the motor-related area was estimated using FreeSurfer for MRI data. The associations between different stages were analysed using Spearman's rho correlation coefficients.

RESULTS

(1) Individually, a smaller volume of right primary motor cortex at T0 was associated with increased MP from T0 to T1 , suggesting the brain's role in changing oral functions; (2) higher MP at T0 was associated with an increased volume of the left superior frontal cortex from T0 to T1 p, suggesting a potential effect on brain plasticity, and (3) increased difficulty to eat was associated with decrease MP but not brain volume of motor-related area.

CONCLUSIONS

The preliminary findings revealed a complicated pattern of structural brain features and masticatory function in elderly people, and either the hypothesis that the brain predisposes masticatory function or the hypothesis that mastication reshapes the brain is oversimplified.

Facial emotion recognition in individuals with mild cognitive impairment: An exploratory study

Understanding facial emotions is fundamental to interact in social environments and modify behavior accordingly. Neurodegenerative processes can progressively transform affective responses and affect social competence. This exploratory study examined the neurocognitive correlates of face recognition, in individuals with two mild cognitive impairment (MCI) etiologies (prodromal to dementia - MCI, or consequent to Parkinson's disease - PD-MCI). Performance on the identification and memorization of neutral and emotional facial expressions was assessed in 31 individuals with MCI, 26 with PD-MCI, and 30 healthy controls (HC). Individuals with MCI exhibited selective impairment in recognizing faces expressing fear, along with difficulties in remembering both neutral and emotional faces. Conversely, individuals with PD-MCI showed no differences compared with the HC in either emotion recognition or memory. In MCI, no significant association emerged between the memory for facial expressions and cognitive difficulties. In PD-MCI, regression analyses showed significant associations with higher-level cognitive functions in the emotional memory task, suggesting the presence of compensatory mechanisms. In a subset of participants, voxel-based morphometry revealed that the performance on emotional tasks correlated with regional changes in gray matter volume. The performance in the matching of negative expressions was predicted by volumetric changes in brain areas engaged in face and emotional processing, in particular increased volume in thalamic nuclei and atrophy in the right parietal cortex. Future studies should leverage on neuroimaging data to determine whether differences in emotional recognition are mediated by pathology-specific atrophic patterns.

The double-edged sword of the hippocampus-ventromedial prefrontal cortex resting-state connectivity in stress susceptibility and resilience: A prospective study

The hippocampus has long been considered a pivotal region implicated in both stress susceptibility and resilience. A wealth of evidence from animal and human studies underscores the significance of hippocampal functional connectivity with the ventromedial prefrontal cortex (vmPFC) in these stress-related processes. However, there remains a scarcity of research that explores and contrasts the roles of hippocampus-vmPFC connectivity in stress susceptibility and resilience when facing a real-life traumatic event from a prospective standpoint. In the present study, we investigated the contributions of undirected and directed connectivity between the hippocampus and vmPFC to stress susceptibility and resilience within the context of the COVID-19 pandemic. Our findings revealed that the left hippocampus-left vmPFC connectivity prior to the pandemic exhibited a negative correlation with both stress susceptibility and resilience. Specifically, individuals with stronger left hippocampus-left vmPFC connectivity reported experiencing fewer stress-related feelings during the outbreak period of the epidemic but displayed lower levels of stress resilience five months later. Our application of spectral dynamic causal modeling unveiled an additional inhibitory connectivity pathway from the left hippocampus to the left vmPFC in the context of stress susceptibility, which was notably absent in stress resilience. Furthermore, we observed a noteworthy positive association between self-inhibition of the vmPFC and stress susceptibility, with this effect proving substantial enough to predict an individual's susceptibility to stress; conversely, these patterns did not manifest in the realm of stress resilience. These findings enrich our comprehension of stress susceptibility and stress resilience and might have implications for innovative approaches to managing stress-related disorders.

Shared molecular genetic factors influence subcortical brain morphometry and Parkinson's disease risk

Parkinson's disease (PD) is a late-onset and genetically complex neurodegenerative disorder. Here we sought to identify genes and molecular pathways underlying the associations between PD and the volume of ten brain structures measured through magnetic resonance imaging (MRI) scans. We leveraged genome-wide genetic data from several cohorts, including the International Parkinson's Disease Genomics Consortium (IPDG), the UK Biobank, the Adolescent Brain Cognitive Development (ABCD) study, the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE), the Enhancing Neuroimaging Genetics through Meta-Analyses (ENIGMA), and 23andMe. We observed significant positive genetic correlations between PD and intracranial and subcortical brain volumes. Genome-wide association studies (GWAS) - pairwise analyses identified 210 genomic segments with shared aetiology between PD and at least one of these brain structures. Pathway enrichment results highlight potential links with chronic inflammation, the hypothalamic-pituitary-adrenal pathway, mitophagy, disrupted vesicle-trafficking, calcium-dependent, and autophagic pathways. Investigations for putative causal genetic effects suggest that a larger putamen volume could influence PD risk, independently of the potential causal genetic effects of intracranial volume (ICV) on PD. Our findings suggest that genetic variants influencing larger intracranial and subcortical brain volumes, possibly during earlier stages of life, influence the risk of developing PD later in life.

Contributions of Cardiovascular Burden, Peripheral Inflammation, and Brain Integrity on Digital Clock Drawing Performance in Non-Demented Older Adults

BACKGROUND

Greater cardiovascular burden and peripheral inflammation are associated with dysexecutive neuropsychological profiles and a higher likelihood of conversion to vascular dementia. The digital clock drawing test (dCDT) is useful in identifying neuropsychological dysfunction related to vascular etiology. However, the specific cognitive implications of the combination of cardiovascular risk, peripheral inflammation, and brain integrity remain unknown.

OBJECTIVE

We aimed to examine the role of cardiovascular burden, inflammation, and MRI-defined brain integrity on dCDT latency and graphomotor metrics in older adults.

METHODS

184 non-demented older adults (age 69±6, 16±3 education years, 46% female, 94% white) completed dCDT, vascular assessment, blood draw, and brain MRI. dCDT variables of interest: total completion time (TCT), pre-first hand latency, digit misplacement, hour hand distance from center, and clock face area. Cardiovascular burden was calculated using the Framingham Stroke Risk Profile (FSRP-10). Peripheral inflammation markers included interleukin (IL)-6, IL-8, IL-10, tumor necrosis factor-alpha, and high sensitivity C-reactive protein. Brain integrity included bilateral entorhinal cortex volume, lateral ventricular volume, and whole brain leukoaraiosis.

RESULTS

FSRP-10, peripheral inflammation, and brain integrity explained an additional 14.6% of the variance in command TCT, where FSRP-10 was the main predictor. FSRP-10, inflammatory markers, and brain integrity explained an additional 17.0% in command digit misplacement variance, with findings largely driven by FSRP-10.

CONCLUSION

Subtle graphomotor behavior operationalized using dCDT metrics (i.e., TCT and digit misplacement) is partly explained by cardiovascular burden, peripheral inflammation, and brain integrity and may indicate vulnerability to a disease process.

Multiclass prediction of different dementia syndromes based on multi-centric volumetric MRI imaging

INTRODUCTION

Dementia syndromes can be difficult to diagnose. We aimed at building a classifier for multiple dementia syndromes using magnetic resonance imaging (MRI).

METHODS

Atlas-based volumetry was performed on T1-weighted MRI data of 426 patients and 51 controls from the multi-centric German Research Consortium of Frontotemporal Lobar Degeneration including patients with behavioral variant frontotemporal dementia, Alzheimer's disease, the three subtypes of primary progressive aphasia, i.e., semantic, logopenic and nonfluent-agrammatic variant, and the atypical parkinsonian syndromes progressive supranuclear palsy and corticobasal syndrome. Support vector machine classification was used to classify each patient group against controls (binary classification) and all seven diagnostic groups against each other in a multi-syndrome classifier (multiclass classification).

RESULTS

The binary classification models reached high prediction accuracies between 71 and 95% with a chance level of 50%. Feature importance reflected disease-specific atrophy patterns. The multi-syndrome model reached accuracies of more than three times higher than chance level but was far from 100%. Multi-syndrome model performance was not homogenous across dementia syndromes, with better performance in syndromes characterized by regionally specific atrophy patterns. Whereas diseases generally could be classified vs controls more correctly with increasing severity and duration, differentiation between diseases was optimal in disease-specific windows of severity and duration.

DISCUSSION

Results suggest that automated methods applied to MR imaging data can support physicians in diagnosis of dementia syndromes. It is particularly relevant for orphan diseases beside frequent syndromes such as Alzheimer's disease.

Criminal arrests associated with reduced regional brain volumes in an adult population with documented childhood lead exposure

Childhood lead exposure interferes with brain maturation, which adversely impacts cognitive and behavioral development. Lower intelligence scores, impairments in decision making, and increased rates of delinquent and criminal behavior are adverse outcomes linked to childhood lead absorption. The present study examined the relationships between childhood blood lead concentrations, structural brain volume, and measures of adult criminality. We hypothesized that increased rates of criminal arrests in adulthood would be inversely correlated with regional gray and white matter volumes, especially prefrontal areas responsible for decision making and self-control. We obtained childhood blood lead histories and anatomical magnetic resonance imaging from a subset of the longitudinally followed birth cohort known as the Cincinnati Lead Study. Criminality data for cohort participants were extracted from public databases. Voxel based morphometry was used to examine spatial differences in regional gray and white matter volumes associated with childhood blood lead concentrations and measures of adult criminality, respectively. Conjunction analyses allowed for the exploratory evaluation of common regions of volume change. Childhood blood lead concentrations were inversely associated with gray and white matter volume in the frontal, parietal and temporal lobes. Gray matter volumes were also inversely associated with criminal arrests with key regions within the cingulate, precuneus, several frontal gyri and the supplementary motor area. Conjunction analyses identified regions in the anterior cingulate, frontal gray matter and supplementary motor area associated with childhood lead absorption and criminality. The results from this study suggest that reduced brain volumes in regions responsible for cognition and emotional regulation are associated with childhood lead exposure and criminal arrests.

Individual voxel-based morphometry adjusting covariates in multiple system atrophy

INTRODUCTION

This study aimed to evaluate whether novel individual voxel-based morphometry adjusting covariates (iVAC), such as age, sex, and total intracranial volume, could increase the accuracy of a diagnosis of multiple system atrophy (MSA) and enable the differentiation of MSA from Parkinson's disease (PD).

METHODS

We included 53 MSA patients (MSA-C: 33, MSA-P: 20), 53 PD patients, and 189 healthy controls in this study. All participants underwent high-resolution T1-weighted imaging (WI) and T2-WI with a 3.0-T MRI scanner. We evaluated the occurrence of significant atrophic findings in the pons/middle cerebellar peduncle (MCP) and putamen on iVAC and compared these findings with characteristic changes on T2-WI.

RESULTS

On iVAC, abnormal findings were observed in the pons/MCP of 96.2% of MSA patients and in the putamen of 80% of MSA patients; however, on T2-WI, they were both observed at a frequency of 60.4% in MSA patients. On iVAC, all but one MSA-P patient (98.1%) showed significant atrophic changes in the pons/MCP or putamen. By contrast, 69.8% of patients with MSA showed abnormal signal changes in the pons/MCP or putamen on T2-WI. iVAC yielded 95.0% sensitivity and 96.2% specificity for differentiating MSA-P from PD.

CONCLUSION

iVAC enabled us to recognize the morphological characteristics of MSA visually and with high accuracy compared to T2-WI, indicating that iVAC is a potential diagnostic screening tool for MSA.

Altered gray matter development in pre-reading children with a family history of reading disorder

Reading disorders are common in children and can impact academic success, mental health, and career prospects. Reading is supported by network of interconnected left hemisphere brain regions, including temporo-parietal, occipito-temporal, and inferior-frontal circuits. Poor readers often show hypoactivation and reduced gray matter volumes in this reading network, with hyperactivation and increased volumes in the posterior right hemisphere. We assessed gray matter development longitudinally in pre-reading children aged 2-5 years using magnetic resonance imaging (MRI) (N = 32, 110 MRI scans; mean age: 4.40 ± 0.77 years), half of whom had a family history of reading disorder. The family history group showed slower proportional growth (relative to total brain volume) in the left supramarginal and inferior frontal gyri, and faster proportional growth in the right angular, right fusiform, and bilateral lingual gyri. This suggests delayed development of left hemisphere reading areas in children with a family history of dyslexia, along with faster growth in right homologues. This alternate development pattern may predispose the brain to later reading difficulties and may later manifest as the commonly noted compensatory mechanisms. The results of this study further shows our understanding of structural brain alterations that may form the neurological basis of reading difficulties.

Exploring Cortical Thickness Alteration in Parkinson Disease Patients with Freezing of Gaits

Background: Freezing of gait (FoG) is a disabling gait disorder that commonly occurs in advanced stages of Parkinson's disease (PD). The neuroanatomical mechanisms underlying FoG in PD are still unclear. The present study aims to explore alterations of structural gray matter (GM) in PD patients with FoG. Method: Twenty-four PD patients with FoG (FoG+), 37 PD patients without FoG (FoG-) and 24 healthy controls (HC) were included. All subjects underwent a standardized MRI protocol. The cortical thickness (CTh), segmentation volume without ventricles (BrainSegVolNotVent) and estimated total intracranial volume (eTIV) were analysed using the FreeSurfer pipeline. Results: CTh differences were found in the right middle temporal gyrus (rMTG) generally. Compared to that in HCs, the CTh of the rMTG in both the FoG+ and FoG- groups was smaller, while no significant difference between the FoG+ and FoG- groups. Correlation analyses demonstrated a negative correlation between the CTh of the rMTG and the UPDRS part II score in PD subjects, and a borderline significant correlation between the score of Freezing of Gait Questionnaire (FoGQ) and rMTG CTh. Additionally, receiver operating characteristic curve (ROC) analysis revealed a cut-off point of CTh =3.08 mm in the rMTG that could be used to differentiate PD patients and HCs (AUC =0.79, P <0.01). There were no differences in the BrainSegVolNotVent or eTIV among the 3 groups. Conclusions: Our findings currently suggest no significant difference between FoG+ and FoG- patients in terms of structural gray matter changes. However, decreased CTh in the rMTG related to semantic control may be used as a biomarker to differentiate PD patients and HCs.

Structural Alterations in Large-scale Brain Networks and Their Relationship with Sleep Disturbances in the Adolescent Population

Although sleep disturbances are highly prevalent in adolescents, neuroimaging evidence on the effects of sleep disturbances on their developing brains remains limited. Therefore, we explored gray matter volumes (GMVs) at the whole-brain level and investigated their relationship to sleep disturbances in a sample of Korean adolescents in the general population. We recruited participants from one middle school and high school. All participants and their legal guardians gave informed consent before participating in our study. We used component 5 of the Pittsburgh Sleep Quality Index to measure sleep disturbances and conducted a voxel-based morphometry-DARTEL procedure to measure GMVs. We performed partial correlation analyses to examine whether the GMVs were associated with sleep disturbances. A total of 56 adolescents participated in this study. Our results revealed that GMVs in multiple global regions were negatively correlated with sleep disturbances. Moreover, most of these identified regions belong to large-scale brain networks categorized by functional neuroimaging studies. We found an association between regional GMVs in multiple global regions involved in large-scale networks and the severity of sleep disturbances in the adolescent population. Based on this evidence and previous neuroimaging evidence, we suggest that structural alterations in the networks may be linked to sleep disturbances.

The quandary of covarying: A brief review and empirical examination of covariate use in structural neuroimaging studies on psychological variables

Although covarying for potential confounds or nuisance variables is common in psychological research, relatively little is known about how the inclusion of covariates may influence the relations between psychological variables and indices of brain structure. In Part 1 of the current study, we conducted a descriptive review of relevant articles from the past two years of NeuroImage in order to identify the most commonly used covariates in work of this nature. Age, sex, and intracranial volume were found to be the most commonly used covariates, although the number of covariates used ranged from 0 to 14, with 37 different covariate sets across the 68 models tested. In Part 2, we used data from the Human Connectome Project to investigate the degree to which the addition of common covariates altered the relations between individual difference variables (i.e., personality traits, psychopathology, cognitive tasks) and regional gray matter volume (GMV), as well as the statistical significance of values associated with these effect sizes. Using traditional and random sampling approaches, our results varied widely, such that some covariate sets influenced the relations between the individual difference variables and GMV very little, while the addition of other covariate sets resulted in a substantially different pattern of results compared to models with no covariates. In sum, these results suggest that the use of covariates should be critically examined and discussed as part of the conversation on replicability in structural neuroimaging. We conclude by recommending that researchers pre-register their analytic strategy and present information on how relations differ based on the inclusion of covariates.

Early limbic microstructural alterations in apathy and depression in de novo Parkinson's disease

BACKGROUND

Whether structural alterations underpin apathy and depression in de novo parkinsonian patients is unknown. The objectives of this study were to investigate whether apathy and depression in de novo parkinsonian patients are related to structural alterations and how structural abnormalities relate to serotonergic or dopaminergic dysfunction.

METHODS

We compared the morphological and microstructural architecture in gray matter using voxel-based morphometry and diffusion tensor imaging coupled with white matter tract-based spatial statistics in a multimodal imaging case-control study enrolling 14 apathetic and 13 nonapathetic patients with de novo Parkinson's disease and 15 age-matched healthy controls, paired with PET imaging of the presynaptic dopaminergic and serotonergic systems.

RESULTS

De novo parkinsonian patients with apathy had bilateral microstructural alterations in the medial corticostriatal limbic system, exhibiting decreased fractional anisotropy and increased mean diffusivity in the anterior striatum and pregenual anterior cingulate cortex in conjunction with serotonergic dysfunction. Furthermore, microstructural alterations extended to the medial frontal cortex, the subgenual anterior cingulate cortex and subcallosal gyrus, the medial thalamus, and the caudal midbrain, suggesting disruption of long-range nondopaminergic projections originating in the brainstem, in addition to microstructural alterations in callosal interhemispheric connections and frontostriatal association tracts early in the disease course. In addition, microstructural abnormalities related to depressive symptoms in apathetic and nonapathetic patients revealed a distinct, mainly right-sided limbic subnetwork involving limbic and frontal association tracts.

CONCLUSIONS

Early limbic microstructural alterations specifically related to apathy and depression emphasize the role of early disruption of ascending nondopaminergic projections and related corticocortical and corticosubcortical networks which underpin the variable expression of nonmotor and neuropsychiatric symptoms in Parkinson's disease. © 2019 International Parkinson and Movement Disorder Society.

Structural brain correlates of fatigue in older adults with and without Parkinson's disease

Fatigue is one of the most common and disabling nonmotor symptoms seen in Parkinson's disease (PD) and is also commonly seen in healthy older adults. Our understanding of the etiology of fatigue in older adults with or without PD is limited and it remains unclear whether fatigue in PD is specifically related to PD pathology. The objective of this study was thus to determine whether fatigue in PD was associated with structural changes in gray or white matter and explore whether these changes were similar in older adults without PD. Magnetic resonance imaging (T₁ weighted) and diffusion tensor imaging were performed in 60 patients with PD (17 females; age = 67.58 ± 5.51; disease duration = 5.67 ± 5.83 years) and 41 age- and sex- matched healthy controls. FSL image processing was used to measure gray matter volume, fractional anisotropy, and leukoariosis differences. Voxel-based morphometry confirmed gray matter loss across the dorsal striatum and insula in the PD patient cohort. PD patients with fatigue had reduced gray matter volume in dorsal striatum relative to PD patients without fatigue (P < 0.05 False Discovery Rate corrected). No significant fatigue-related structural atrophy was found in controls. There were no areas of significant fractional anisotropy differences between high and low fatigue subjects in either the PD or non-PD groups. Control participants with high fatigue, but not PD, showed significantly greater total leukoariosis volumes (p = 0.03). Fatigue in PD is associated with unique structural changes in the caudate and putamen suggesting fatigue in PD is primarily related to PD pathology, particularly in the dorsal striatum, and not simply a consequence of aging.

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Fatigue is a disabling symptom in Parkinson's disease (PD) and healthy older adults.

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We studied structural correlates of fatigue using MRI morphometry.

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PD patients with high fatigue had caudate atrophy.

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Healthy older adults with fatigue had increased burden of leukoariosis.

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Patterns of structural brain changes were distinct between PD and healthy group.

Identifying brain changes related to cognitive aging using VBM and visual rating scales

Aging is often associated with changes in brain structures as well as in cognitive functions. Structural changes can be visualized with Magnetic Resonance Imaging (MRI) using voxel-based grey matter morphometry (VBM) and visual rating scales to assess atrophy level. Several MRI studies have shown that possible neural correlates of cognitive changes can be seen in normal aging. It is still not fully understood how cognitive function as measured by tests and demographic factors are related to brain changes in the MRI. We recruited 55 healthy elderly subjects aged 50–79 years. A battery of cognitive tests was administered to all subjects prior to MRI scanning. Our aim was to assess correlations between age, sex, education, cognitive test performance, and the said two MRI-based measures. Our results show significant differences in VBM grey matter volume for education level (≤ 12 vs. > 12 years), with a smaller amount of grey matter volume in subjects with lower educational levels, and for age in interaction with education, indicating larger grey matter volume for young, higher educated adults. Also, grey matter volume was found to be correlated with working memory function (Digit Span Backward). Furthermore, significant positive correlations were found between visual ratings and both age and education, showing larger atrophy levels with increasing age and decreasing level of education. These findings provide supportive evidence that MRI-VBM detects structural differences for education level, and correlates with educational level and age, and working memory task performance.

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VBM grey matter volume differences were significant for the interaction of age and education level.

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Grey matter volume correlated with education level and working memory function (Digit Span Backward).

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Significant correlations were found between visual rating scales and both age and education.

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VBM is able to detect structural differences for age and education, and correlates with education and working memory.

Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning

In the literature, there are substantial machine learning attempts to classify schizophrenia based on alterations in resting-state (RS) brain patterns using functional magnetic resonance imaging (fMRI). Most earlier studies modelled patients undergoing treatment, entailing confounding with drug effects on brain activity, and making them less applicable to real-world diagnosis at the point of first medical contact. Further, most studies with classification accuracies >80% are based on small sample datasets, which may be insufficient to capture the heterogeneity of schizophrenia, limiting generalization to unseen cases. In this study, we used RS fMRI data collected from a cohort of antipsychotic drug treatment-naive patients meeting DSM IV criteria for schizophrenia (N = 81) as well as age- and sex-matched healthy controls (N = 93). We present an ensemble model -- EMPaSchiz (read as ‘Emphasis’; standing for ‘Ensemble algorithm with Multiple Parcellations for Schizophrenia prediction’) that stacks predictions from several ‘single-source’ models, each based on features of regional activity and functional connectivity, over a range of different a priori parcellation schemes. EMPaSchiz yielded a classification accuracy of 87% (vs. chance accuracy of 53%), which out-performs earlier machine learning models built for diagnosing schizophrenia using RS fMRI measures modelled on large samples (N > 100). To our knowledge, EMPaSchiz is first to be reported that has been trained and validated exclusively on data from drug-naive patients diagnosed with schizophrenia. The method relies on a single modality of MRI acquisition and can be readily scaled-up without needing to rebuild parcellation maps from incoming training images.

Aggression and Anabolic-Androgenic Steroid Use Intent in Adolescents: A Longitudinal Study

Background: There is a dearth of longitudinal studies on risk factors for anabolic-androgenic steroid (AAS) use and intent. Objectives: We conducted a longitudinal investigation of factors associated with AAS use intent from age 18 to 19, examining particularly the role of physical and verbal aggression. A sample of Norwegians completed questionnaires containing demographic, AAS use and intent, other addictions, aggression, and health measures at ages 18 (N = 1333, females = 58.9%) and 19 (N = 1277, females = 61.7%). The data were analyzed using descriptive statistics, correlations, and hierarchical multiple regression. Results: Results show that AAS use prevalence increased from 1 person at age 18 to 4 persons at age 19. Intent to use AAS, being male, living alone, and AAS use (all at 18 years) were associated with higher AAS use intent at 19 years. We found no associations between physical or verbal aggression and AAS use intent from ages 18 to 19. Conclusions: Our findings indicate temporal stability in AAS use intent, as well as the influence of demographic and health factors on AAS use intent from ages 18 to 19. Implications of findings for practice and future research are discussed.

Cognition and connectomes in nondementia idiopathic Parkinson's disease

In this study, we investigate the organization of the structural connectome in cognitively well participants with Parkinson’s disease (PD-Well; n = 31) and a subgroup of participants with Parkinson’s disease who have amnestic disturbances (PD-MI; n = 9). We explore correlations between connectome topology and vulnerable cognitive domains in Parkinson’s disease relative to non-Parkinson’s disease peers (control, n = 40). Diffusion-weighted MRI data and deterministic tractography were used to generate connectomes. Connectome topological indices under study included weighted indices of node strength, path length, clustering coefficient, and small-worldness. Relative to controls, node strength was reduced 4.99% for PD-Well (p = 0.041) and 13.2% for PD-MI (p = 0.004). We found bilateral differences in the node strength between PD-MI and controls for inferior parietal, caudal middle frontal, posterior cingulate, precentral, and rostral middle frontal. Correlations between connectome and cognitive domains of interest showed that topological indices of global connectivity negatively associated with working memory and displayed more and larger negative correlations with neuropsychological indices of memory in PD-MI than in PD-Well and controls. These findings suggest that indices of network connectivity are reduced in PD-MI relative to PD-Well and control participants.

Parkinson’s disease (PD) patients with amnestic mild cognitive impairment (e.g., primary processing-speed impairments or primary memory impairments) are at greater risk of developing dementia. Recent evidence suggests that patients with PD and mild cognitive impairment present an altered connectome connectivity. In this work, we further explore the structural connectome of PD patients to provide clues to identify possible sensitive markers of disease progression, and cognitive impairment, in susceptible PD patients. We employed a weighted network framework that yields more stable topological results than the binary network framework and is robust despite graph density differences, hence it does not require thresholding to analyze the connectomes. As Supplementary Information (Colon-Perez et al., 2017), we include databases sharing the results of the network data.