Atypical co-development of the thalamus and cortex in autism: Evidence from age-related white-gray contrast change.

Recent studies have shown that white-gray contrast (WGC) of either cortical or subcortical gray matter provides for accurate predictions of age in typically developing (TD) children, and that, at least for the cortex, it changes differently with age in subjects with autism spectrum disorder (ASD) compared to their TD peers. Our previous study showed different patterns of contrast change between ASD and TD in sensorimotor and association cortices. While that study was confined to the cortex, we hypothesized that subcortical structures, particularly the thalamus, were involved in the observed cortical dichotomy between lower and higher processing. The current paper investigates that hypothesis using the WGC measures from the thalamus in addition to those from the cortex. We compared age-related WGC changes in the thalamus to those in the cortex. To capture the simultaneity of this change across the two structures, we devised a metric capturing the co-development of the thalamus and cortex (CoDevTC), proportional to the magnitude of cortical and thalamic age-related WGC change. We calculated this metric for each of the subjects in a large homogeneous sample taken from the Autism Brain Imaging Data Exchange (ABIDE) (N = 434). We used structural MRI data from the largest high-quality cross-sectional sample (NYU) as well as two other large high-quality sites, GU and OHSU, all three using Siemens 3T scanners. We observed that the co-development features in ASD and TD exhibit contrasting patterns; specifically, some higher-order thalamic nuclei, such as the lateral dorsal nucleus, exhibited reduction in codevelopment with most of the cortex in ASD compared to TD. Moreover, this difference in the CoDevTC pattern correlates with a number of behavioral measures across multiple cognitive and physiological domains. The results support previous notions of altered connectivity in autism, but add more specific evidence about the heterogeneity in thalamocortical development that elucidates the mechanisms underlying the clinical features of ASD.

Network structure and transcriptomic vulnerability shape atrophy in frontotemporal dementia.

Connections among brain regions allow pathological perturbations to spread from a single source region to multiple regions. Patterns of neurodegeneration in multiple diseases, including behavioural variant of frontotemporal dementia (bvFTD), resemble the large-scale functional systems, but how bvFTD-related atrophy patterns relate to structural network organization remains unknown. Here we investigate whether neurodegeneration patterns in sporadic and genetic bvFTD are conditioned by connectome architecture. Regional atrophy patterns were estimated in both genetic bvFTD (75 patients, 247 controls) and sporadic bvFTD (70 patients, 123 controls). First, we identified distributed atrophy patterns in bvFTD, mainly targeting areas associated with the limbic intrinsic network and insular cytoarchitectonic class. Regional atrophy was significantly correlated with atrophy of structurally- and functionally-connected neighbours, demonstrating that network structure shapes atrophy patterns. The anterior insula was identified as the predominant group epicentre of brain atrophy using data-driven and simulation-based methods, with some secondary regions in frontal ventromedial and antero-medial temporal areas. We found that FTD-related genes, namely C9orf72 and TARDBP, confer local transcriptomic vulnerability to the disease, modulating the propagation of pathology through the connectome. Collectively, our results demonstrate that atrophy patterns in sporadic and genetic bvFTD are jointly shaped by global connectome architecture and local transcriptomic vulnerability, providing an explanation as to how heterogenous pathological entities can lead to the same clinical syndrome.

Prenatal maternal depressive symptoms are associated with neonatal left amygdala microstructure in a sex-dependent way.

Exposures to prenatal maternal depressive symptoms (PMDS) may lead to neurodevelopmental changes in the offspring in a sex-dependent way. Although a connection between PMDS and infant brain development has been established by earlier studies, the relationship between PMDS exposures measured at various prenatal stages and microstructural alterations in fundamental subcortical structures such as the amygdala remains unknown. In this study, we investigated the associations between PMDS measured during gestational weeks 14, 24 and 34 and infant amygdala microstructural properties using diffusion tensor imaging. We explored amygdala mean diffusivity (MD) alterations in response to PMDS in infants aged 11 to 54 days from birth. PMDS had no significant main effect on the amygdala MD metrics. However, there was a significant interaction effect for PMDS and infant sex in the left amygdala MD. Compared with girls, boys exposed to greater PMDS during gestational week 14 showed significantly higher left amygdala MD. These results indicate that PMDS are linked to infants' amygdala microstructure in boys. These associations may be relevant to later neuropsychiatric outcomes in the offspring. Further research is required to better understand the mechanisms underlying these associations and to develop effective interventions to counteract any potential adverse consequences.

Bloody noise: The impact of blood-flow artifacts on registration.

Blood-flow artifacts present a serious challenge for most, if not all, volumetric analytical approaches. We utilize T1-weighted data with prominent blood-flow artifacts from the Autism Brain Imaging Data Exchange (ABIDE) multisite agglomerative dataset to assess the impact that such blood-flow artifacts have on registration of T1-weighted data to a template. We use a heuristic approach to identify the blood-flow artifacts in these data; we use the resulting blood masks to turn the underlying voxels to the intensity of the cerebro-spinal fluid, thus mimicking the effect of blood suppression. We then register both the original data and the deblooded data to a common T1-weighted template, and compare the quality of those registrations to the template in terms of similarity to the template. The registrations to the template based on the deblooded data yield significantly higher similarity values compared with those based on the original data. Additionally, we measure the nonlinear deformations needed to transform the data from the position achieved by registering the original data to the template to the position achieved by registering the deblooded data to the template. The results indicate that blood-flow artifacts may seriously impact data processing that depends on registration to a template, that is, most all data processing.

Hippocampal grading provides higher classification accuracy for those in the AD trajectory than hippocampal volume.

Much research has focused on neurodegeneration in aging and Alzheimer's disease (AD). We developed Scoring by Nonlocal Image Patch Estimator (SNIPE), a non-local patch-based measure of anatomical similarity and hippocampal segmentation to measure hippocampal change. While SNIPE shows enhanced predictive power over hippocampal volume, it is unknown whether SNIPE is more strongly associated with group differences between normal controls (NC), early MCI (eMCI), late (lMCI), and AD than hippocampal volume. Alzheimer's Disease Neuroimaging Initiative older adults were included in the first analyses (N = 1666, 513 NCs, 269 eMCI, 556 lMCI, and 328 AD). Sub-analyses investigated amyloid positive individuals (N = 834; 179 NC, 148 eMCI, 298 lMCI, and 209 AD) to determine accuracy in those on the AD trajectory. We compared SNIPE grading, SNIPE volume, and Freesurfer volume as features in seven different machine learning techniques classifying participants into their correct cohort using 10-fold cross-validation. The best model was then validated in the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL). SNIPE grading provided the highest classification accuracy for all classifications in both the full and amyloid positive sample. When classifying NC:AD, SNIPE grading provided an 89% accuracy (full sample) and 87% (amyloid positive sample). Freesurfer volume provided much lower accuracies of 65% (full sample) and 46% (amyloid positive sample). In the AIBL validation cohort, SNIPE grading provided a 90% classification accuracy for NC:AD. These findings suggest SNIPE grading provides increased classification accuracy over both SNIPE and Freesurfer volume. SNIPE grading offers promise to accurately identify people with and without AD.

The use of hippocampal grading as a biomarker for preclinical and prodromal Alzheimer's disease.

Hippocampal changes are associated with increased age and cognitive decline due to mild cognitive impairment (MCI) and Alzheimer's disease (AD). These associations are often observed only in the later stages of decline. This study examined if hippocampal grading, a method measuring local morphological similarity of the hippocampus to cognitively normal controls (NCs) and AD participants, is associated with cognition in NCs, subjective cognitive decline (SCD), early (eMCI), late (lMCI), and AD. A total of 1620 Alzheimer's Disease Neuroimaging Initiative participants were examined (495 NC, 262 eMCI, 545 lMCI, and 318 AD) because they had baseline MRIs and Alzheimer's disease Assessment Scale (ADAS-13) and Clinical Dementia Rating-Sum of Boxes (CDR-SB) scores. In a sub-analysis, NCs with episodic memory scores (as measured by Rey Auditory Verbal Learning Test, RAVLT) were divided into those with subjective cognitive decline (SCD+; 103) and those without (SCD-; 390). Linear regressions evaluated the influence of hippocampal grading on cognition in preclinical and prodromal AD. Lower global cognition, as measured by increased ADAS-13, was associated with hippocampal grading: NC (p < .001), eMCI (p < .05), lMCI (p < .05), and AD (p = .01). Lower global cognition as measured increased CDR-SB was associated with hippocampal grading in lMCI (p < .05) and AD (p < .001). Lower RAVLT performance was associated with hippocampal grading in SCD- (p < .05) and SCD+ (p < .05). These findings suggest that hippocampal grading is associated with global cognition in NC, eMCI, lMCI, and AD. Early changes in episodic memory during pre-clinical AD are associated with changes in hippocampal grading. Hippocampal grading may be sensitive to progressive changes early in the disease course.

Diffusely abnormal white matter converts to T2 lesion volume in the absence of MRI-detectable acute inflammation.

Diffusely abnormal white matter, characterised by biochemical changes of myelin in the absence of frank demyelination, has been associated with clinical progression in secondary progressive multiple sclerosis. However, little is known about changes of diffusely abnormal white matter over time and their relation to focal white matter lesions. The objectives of this work were: (i) to characterize the longitudinal evolution of focal white matter lesions, diffusely abnormal white matter and diffusely abnormal white matter that transforms into focal white matter lesions; and (ii) to determine whether gadolinium enhancement, known to be associated with the development of new focal white matter lesions, is also related to diffusely abnormal white matter voxels that transform into focal white matter lesions. Our data included 4220 MRI scans of 689 secondary progressive multiple sclerosis participants, followed for 156 weeks, and 2677 scans of 686 relapsing-remitting multiple sclerosis participants, followed for 96 weeks. Focal white matter lesions and diffusely abnormal white matter were segmented using a previously validated, automatic thresholding technique based on normalized T2 intensity values. Using longitudinally registered images, diffusely abnormal white matter voxels at each visit that transformed into focal white matter lesions on the last MRI scan as well as their overlap with gadolinium-enhancing lesion masks were identified. Our results showed that the average yearly rate of conversion of diffusely abnormal white matter to focal white matter lesions was 1.27 cm3 for secondary progressive multiple sclerosis and 0.80 cm3 for relapsing-remitting multiple sclerosis. Focal white matter lesions in secondary progressive multiple sclerosis participants significantly increased (t = 3.9; P = 0.0001) while diffusely abnormal white matter significantly decreased (t = -4.3 P < 0.0001) and the ratio of focal white matter lesions to diffusely abnormal white matter increased (t = 12.7; P < 0.00001). Relapsing-remitting multiple sclerosis participants also showed an increase in the focal white matter lesions to diffusely abnormal white matter ratio (t = 6.9; P < 0.00001) but without a significant change of the individual volumes. Gadolinium enhancement was associated with 7.3% and 18.7% of focal new T2 lesion formation in the infrequent scans of the relapsing-remitting multiple sclerosis and secondary progressive multiple sclerosis cohorts, respectively. In comparison, only 0.1% and 0.0% of diffusely abnormal white matter to focal white matter lesions voxels overlapped with gadolinium enhancement. We conclude that diffusely abnormal white matter transforms into focal white matter lesions over time in both relapsing-remitting multiple sclerosis and secondary progressive multiple sclerosis. Diffusely abnormal white matter appears to represent a form of pre-lesional pathology that contributes to T2 lesion volume increase over time, independent of new focal inflammation and gadolinium enhancement.

Early brain development in infants at high risk for autism spectrum disorder.

Brain enlargement has been observed in children with autism spectrum disorder (ASD), but the timing of this phenomenon, and the relationship between ASD and the appearance of behavioural symptoms, are unknown. Retrospective head circumference and longitudinal brain volume studies of two-year olds followed up at four years of age have provided evidence that increased brain volume may emerge early in development. Studies of infants at high familial risk of autism can provide insight into the early development of autism and have shown that characteristic social deficits in ASD emerge during the latter part of the first and in the second year of life. These observations suggest that prospective brain-imaging studies of infants at high familial risk of ASD might identify early postnatal changes in brain volume that occur before an ASD diagnosis. In this prospective neuroimaging study of 106 infants at high familial risk of ASD and 42 low-risk infants, we show that hyperexpansion of the cortical surface area between 6 and 12 months of age precedes brain volume overgrowth observed between 12 and 24 months in 15 high-risk infants who were diagnosed with autism at 24 months. Brain volume overgrowth was linked to the emergence and severity of autistic social deficits. A deep-learning algorithm that primarily uses surface area information from magnetic resonance imaging of the brain of 6-12-month-old individuals predicted the diagnosis of autism in individual high-risk children at 24 months (with a positive predictive value of 81% and a sensitivity of 88%). These findings demonstrate that early brain changes occur during the period in which autistic behaviours are first emerging.

Association of cumulative prenatal adversity with infant subcortical structure volumes and child problem behavior and its moderation by a coexpression polygenic risk score of the serotonin system.

Prenatal adversity has been linked to later psychopathology. Yet, research on cumulative prenatal adversity, as well as its interaction with offspring genotype, on brain and behavioral development is scarce. With this study, we aimed to address this gap. In Finnish mother-infant dyads, we investigated the association of a cumulative prenatal adversity sum score (PRE-AS) with (a) child emotional and behavioral problems assessed with the Strengths and Difficulties Questionnaire at 4 and 5 years (N = 1568, 45.3% female), (b) infant amygdalar and hippocampal volumes (subsample N = 122), and (c) its moderation by a hippocampal-specific coexpression polygenic risk score based on the serotonin transporter (SLC6A4) gene. We found that higher PRE-AS was linked to greater child emotional and behavioral problems at both time points, with partly stronger associations in boys than in girls. Higher PRE-AS was associated with larger bilateral infant amygdalar volumes in girls compared to boys, while no associations were found for hippocampal volumes. Further, hyperactivity/inattention in 4-year-old girls was related to both genotype and PRE-AS, the latter partially mediated by right amygdalar volumes as preliminary evidence suggests. Our study is the first to demonstrate a dose-dependent sexually dimorphic relationship between cumulative prenatal adversity and infant amygdalar volumes.

A state-of-the-art review on deep learning for estimating eloquent cortex from resting-state fMRI.

Deep learning algorithms have greatly improved our ability to estimate eloquent cortex regions from resting-state brain scans for patients about to undergo neurosurgery. The use of deep learning has the potential to fully automate functional mapping of cortex in this context. We present a highly focused state-of-the-art review on current technology for estimating eloquent cortex from resting-state functional magnetic resonance scans and identify potential paths to meet this goal in the future.

DARQ: Deep learning of quality control for stereotaxic registration of human brain MRI to the T1w MNI-ICBM 152 template.

Linear registration to stereotaxic space is a common first step in many automated image-processing tools for analysis of human brain MRI scans. This step is crucial for the success of the subsequent image-processing steps. Several well-established algorithms are commonly used in the field of neuroimaging for this task, but none have a 100% success rate. Manual assessment of the registration is commonly used as part of quality control. To reduce the burden of this time-consuming step, we propose Deep Automated Registration Qc (DARQ), a fully automatic quality control method based on deep learning that can replace the human rater and accurately perform quality control assessment for stereotaxic registration of T1w brain scans. In a recently published study from our group comparing linear registration methods, we used a database of 9325 MRI scans and 64476 registrations from several publicly available datasets and applied seven linear registration tools to them. In this study, the resulting images that were assessed and labeled by a human rater are used to train a deep neural network to detect cases when registration failed. We further validated the results on an independent dataset of patients with multiple sclerosis, with manual QC labels available (n=1200). In terms of agreement with a manual rater, our automated QC method was able to achieve 89% accuracy and 85% true negative rate (equivalently 15% false positive rate) in detecting scans that should pass quality control in a balanced cross-validation experiments, and 96.1% accuracy and 95.5% true negative rate (or 4.5% FPR) when evaluated in a balanced independent sample, similar to manual QC rater (test-retest accuracy of 93%). The results show that DARQ is robust, fast, accurate, and generalizable in detecting failure in linear stereotaxic registrations and can substantially reduce QC time (by a factor of 20 or more) when processing large datasets.

A sub+cortical fMRI-based surface parcellation.

Both cortical and subcortical structures are organized into a large number of distinct areas reflecting functional and cytoarchitectonic differences. Mapping these areas is of fundamental importance to neuroscience. A central obstacle to this task is the inaccuracy associated with bringing results from individuals into a common space. The vast individual differences in morphology pose a serious problem for volumetric registration. Surface-based approaches fare substantially better, but have thus far been used only for cortical parcellation, leaving subcortical parcellation in volumetric space. We extend the surface-based approach to include also the subcortical deep gray-matter structures, thus achieving a uniform representation across both cortex and subcortex, suitable for use with surface-based metrics that span these structures, for example, white/gray contrast. Using data from the Enhanced Nathan Klein Institute-Rockland Sample, limited to individuals between 19 and 69 years of age, we generate a functional parcellation of both the cortical and subcortical surfaces. To assess this extended parcellation, we show that (a) our parcellation provides greater homogeneity of functional connectivity patterns than do arbitrary parcellations matching in the number and size of parcels; (b) our parcels align with known cortical and subcortical architecture; and (c) our extended functional parcellation provides an improved fit to the complexity of life-span (6-85 years) changes in white/gray contrast data compared to arbitrary parcellations matching in the number and size of parcels, supporting its use with surface-based measures. We provide our extended functional parcellation for the use of the neuroimaging community.

Allometry in the corpus callosum in neonates: Sexual dimorphism.

The corpus callosum (CC) is the largest fiber tract in the human brain, allowing interhemispheric communication by connecting homologous areas of the two cerebral hemispheres. In adults, CC size shows a robust allometric relationship with brain size, with larger brains having larger callosa, but smaller brains having larger callosa relative to brain size. Such an allometric relationship has been shown in both males and females, with no significant difference between the sexes. But there is some evidence that there are alterations in these allometric relationships during development. However, it is currently not known whether there is sexual dimorphism in these allometric relationships from birth, or if it only develops later. We study this in neonate data. Our results indicate that there are already sex differences in these allometric relationships in neonates: male neonates show the adult-like allometric relationship between CC size and brain size; however female neonates show a significantly more positive allometry between CC size and brain size than either male neonates or female adults. The underlying cause of this sexual dimorphism is unclear; but the existence of this sexual dimorphism in neonates suggests that sex-differences in lateralization have prenatal origins.

Relationship between impulsivity, uncontrolled eating and body mass index: a hierarchical model.

BACKGROUND: Impulsivity increases the risk for obesity and weight gain. However, the precise role of impulsivity in the aetiology of overeating behavior and obesity is currently unknown. Here we examined the relationships between personality-related measures of impulsivity, Uncontrolled Eating, body mass index (BMI), and longitudinal weight changes. In addition, we analyzed the associations between general impulsivity domains and cortical thickness to elucidate brain vulnerability factors related to weight gain. METHODS: Students (N = 2318) in their first year of university-a risky period for weight gain-completed questionnaire measures of impulsivity and eating behavior at the beginning of the school year. We also collected their weight at the end of the term (N = 1177). Impulsivity was divided into three factors: stress reactivity, reward sensitivity and lack of self-control. Using structural equation models, we tested a hierarchical relationship, in which impulsivity traits were associated with Uncontrolled Eating, which in turn predicted BMI and weight change. Seventy-one participants underwent T1-weighted MRI to investigate the correlation between impulsivity and cortical thickness. RESULTS: Impulsivity traits showed positive correlations with Uncontrolled Eating. Higher scores in Uncontrolled Eating were in turn associated with higher BMI. None of the impulsivity-related measurements nor Uncontrolled Eating were correlated with longitudinal weight gain. Higher stress sensitivity was associated with increased cortical thickness in the superior temporal gyrus. Lack of self-control was positively associated with increased thickness in the superior medial frontal gyrus. Finally, higher reward sensitivity was associated with lower thickness in the inferior frontal gyrus. CONCLUSION: The present study provides a comprehensive characterization of the relationships between different facets of impulsivity and obesity. We show that differences in impulsivity domains might be associated with BMI via Uncontrolled Eating. Our results might inform future clinical strategies aimed at fostering self-control abilities to prevent and/or treat unhealthy weight gain.

Regional Cerebellar Volume Loss Predicts Future Disability in Multiple Sclerosis Patients.

Cerebellar symptoms in multiple sclerosis (MS) are well described; however, the exact contribution of cerebellar damage to MS disability has not been fully explored. Longer-term observational periods are necessary to better understand the dynamics of pathological changes within the cerebellum and their clinical consequences. Cerebellar lobe and single lobule volumes were automatically segmented on 664 3D-T1-weighted MPRAGE scans (acquired at a single 1.5 T scanner) of 163 MS patients (111 women; mean age: 47.1 years; 125 relapsing-remitting (RR) and 38 secondary progressive (SP) MS, median EDSS: 3.0) imaged annually over 4 years. Clinical scores (EDSS, 9HPT, 25FWT, PASAT, SDMT) were determined per patient per year with a maximum clinical follow-up of 11 years. Linear mixed-effect models were applied to assess the association between cerebellar volumes and clinical scores and whether cerebellar atrophy measures may predict future disability progression. SPMS patients exhibited faster posterior superior lobe volume loss over time compared to RRMS, which was related to increase of EDSS over time. In RRMS, cerebellar volumes were significant predictors of motor scores (e.g. average EDSS, T25FWT and 9HPT) and SDMT. Atrophy of motor-associated lobules (IV-VI + VIII) was a significant predictor of future deterioration of the 9HPT of the non-dominant hand. In SPMS, the atrophy rate of the posterior superior lobe (VI + Crus I) was a significant predictor of future PASAT performance deterioration. Regional cerebellar volume reduction is associated with motor and cognitive disability in MS and may serve as a predictor for future disease progression, especially of dexterity and impaired processing speed.

Sex-specific associations between maternal pregnancy-specific anxiety and newborn amygdalar volumes - preliminary findings from the FinnBrain Birth Cohort Study.

Previous literature links maternal pregnancy-specific anxiety (PSA) with later difficulties in child emotional and social cognition as well as memory, functions closely related to the amygdala and the hippocampus. Some evidence also suggests that PSA affects child amygdalar volumes in a sex-dependent way. However, no studies investigating the associations between PSA and newborn amygdalar and hippocampal volumes have been reported. We investigated the associations between PSA and newborn amygdalar and hippocampal volumes and whether associations are sex-specific in 122 healthy newborns (68 males/54 females) scanned at 2-5 weeks postpartum. PSA was measured at gestational week 24 with the Pregnancy-Related Anxiety Questionnaire Revised 2 (PRAQ-R2). The associations were analyzed with linear regression controlling for confounding variables. PSA was associated positively with left amygdalar volume in girls, but no significant main effect was found in the whole group or in boys. No significant main or sex-specific effect was found for hippocampal volumes. Although this was an exploratory study, the findings suggest a sexually dimorphic association of mid-pregnancy PSA with newborn amygdalar volumes.

BISON: Brain tissue segmentation pipeline using T1 -weighted magnetic resonance images and a random forest classifier.

PURPOSE: Tissue segmentation from T1 -weighted (T1W) MRI is a critical requirement in many neuroscience and clinical applications. However, accurate tissue segmentation is challenging because of the variabilities in tissue intensity profiles caused by differences in scanner models, acquisition protocols, and age. In addition, many methods assume healthy anatomy and fail in the presence of pathology such as white matter hyperintensities (WMHs). We present BISON (Brain tISsue segmentatiON), a new pipeline for tissue segmentation using a random forest classifier and a set of intensity and location priors based on T1W MRI. METHODS: BISON was developed and cross-validated using multiscanner manual labels of 72 subjects aged 5 to 96 years. We also assessed the test-retest reliability of BISON on two data sets: 20 subjects with scan/rescan MR images and manual segmentations and 90 scans from a single individual. The results were compared against Atropos, a state-of-the-art commonly used tissue classification method from advanced normalization tools (ANTs). RESULTS: BISON cross-validation dice kappa values against manual segmentations of 72 MRI volumes yielded κGM = 0.88, κWM = 0.85, κCSF = 0.77, outperforming Atropos (κGM = 0.79, κWM = 0.84, κCSF = 0.64), test-retest values on 20 subjects of κGM = 0.94, κWM = 0.92, κCSF = 0.77 outperforming both manual (κGM = 0.92, κWM = 0.91, κCSF =0.74) and Atropos (κGM = 0.87, κWM = 0.92, κCSF = 0.79). Finally, BISON outperformed Atropos, FAST (fast automated segmentation tool) from the FMRIB (Functional Magnetic Resonance Imaging of the Brain) Software Library, and SPM12 (statistical parametric mapping 12) in the presence of WMHs. CONCLUSION: BISON can provide accurate and robust segmentations in data from various age ranges and scanner models, making it ideal for performing tissue classification in large multicenter and multiscanner databases.

MRI data-driven algorithm for the diagnosis of behavioural variant frontotemporal dementia.

INTRODUCTION: Structural brain imaging is paramount for the diagnosis of behavioural variant of frontotemporal dementia (bvFTD), but it has low sensitivity leading to erroneous or late diagnosis. METHODS: A total of 515 subjects from two different bvFTD cohorts (training and independent validation cohorts) were used to perform voxel-wise morphometric analysis to identify regions with significant differences between bvFTD and controls. A random forest classifier was used to individually predict bvFTD from deformation-based morphometry differences in isolation and together with semantic fluency. Tenfold cross validation was used to assess the performance of the classifier within the training cohort. A second held-out cohort of genetically confirmed bvFTD cases was used for additional validation. RESULTS: Average 10-fold cross-validation accuracy was 89% (82% sensitivity, 93% specificity) using only MRI and 94% (89% sensitivity, 98% specificity) with the addition of semantic fluency. In the separate validation cohort of definite bvFTD, accuracy was 88% (81% sensitivity, 92% specificity) with MRI and 91% (79% sensitivity, 96% specificity) with added semantic fluency scores. CONCLUSION: Our results show that structural MRI and semantic fluency can accurately predict bvFTD at the individual subject level within a completely independent validation cohort coming from a different and independent database.

Conversion of diffusely abnormal white matter to focal lesions is linked to progression in secondary progressive multiple sclerosis.

BACKGROUND: Diffusely abnormal white matter (DAWM) regions are observed in magnetic resonance images of secondary progressive multiple sclerosis (SPMS) patients. However, their role in clinical progression is still not established. OBJECTIVES: To characterize the longitudinal volumetric and intensity evolution of DAWM and focal white matter lesions (FWML) and assess their associations with clinical outcomes and progression in SPMS. METHODS: Data include 589 SPMS participants followed up for 3 years (3951 time points). FWML and DAWM were automatically segmented. Screening DAWM volumes that transformed into FWML at the last visit (DAWM-to-FWML) and normalized T1-weighted intensities (indicating severity of damage) in those voxels were calculated. RESULTS: FWML volume increased and DAWM volume decreased with an increase in disease duration (p < 0.001). The Expanded Disability Status Scale (EDSS) was positively associated with FWML volumes (p = 0.002), but not with DAWM. DAWM-to-FWML volume was higher in patients who progressed (2.75 cm3 vs. 1.70 cm3; p < 0.0001). Normalized T1-weighted intensity of DAWM-to-FWML was negatively associated with progression (p < 0.00001). CONCLUSION: DAWM transformed into FWML over time, and this transformation was associated with clinical progression. DAWM-to-FWML voxels had greater normalized T1-weighted intensity decrease over time, in keeping with relatively greater tissue damage. Evaluation of DAWM in progressive multiple sclerosis provides a useful measure for therapies aiming to protect this at-risk tissue with the potential to slow progression.

Morphometric network differences in ageing versus Alzheimer's disease dementia.

Age being the main risk factor for Alzheimer's disease, it is particularly challenging to disentangle structural changes related to normal brain ageing from those specific to Alzheimer's disease. Most studies aiming to make this distinction focused on older adults only and on a priori anatomical regions. Drawing on a large, multi-cohort dataset ranging from young adults (n = 468; age range 18-35 years), to older adults with intact cognition (n = 431; age range 55-90 years) and with Alzheimer's disease (n = 50 with late mild cognitive impairment and 71 with Alzheimer's dementia, age range 56-88 years), we investigated grey matter organization and volume differences in ageing and Alzheimer's disease. Using independent component analysis on all participants' structural MRI, we first derived morphometric networks and extracted grey matter volume in each network. We also derived a measure of whole-brain grey matter pattern organization by correlating grey matter volume in all networks across all participants from the same cohort. We used logistic regressions and receiver operating characteristic analyses to evaluate how well grey matter volume in each network and whole-brain pattern could discriminate between ageing and Alzheimer's disease. Because increased heterogeneity is often reported as one of the main features characterizing brain ageing, we also evaluated interindividual heterogeneity within morphometric networks and across the whole-brain organization in ageing and Alzheimer's disease. Finally, to investigate the clinical validity of the different grey matter features, we evaluated whether grey matter volume or whole-brain pattern was related to clinical progression in cognitively normal older adults. Ageing and Alzheimer's disease contributed additive effects on grey matter volume in nearly all networks, except frontal lobe networks, where differences in grey matter were more specific to ageing. While no networks specifically discriminated Alzheimer's disease from ageing, heterogeneity in grey matter volumes across morphometric networks and in the whole-brain grey matter pattern characterized individuals with cognitive impairments. Preservation of the whole-brain grey matter pattern was also related to lower risk of developing cognitive impairment, more so than grey matter volume. These results suggest both ageing and Alzheimer's disease involve widespread atrophy, but that the clinical expression of Alzheimer's disease is uniquely associated with disruption of morphometric organization.