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Background Persistent sensorimotor impairments after stroke can negatively impact quality of life. The hippocampus is vulnerable to poststroke secondary degeneration and is involved in sensorimotor behavior but has not been widely studied within the context of poststroke upper-limb sensorimotor impairment. We investigated associations between non-lesioned hippocampal volume and upper limb sensorimotor impairment in people with chronic stroke, hypothesizing that smaller ipsilesional hippocampal volumes would be associated with greater sensorimotor impairment. Methods and Results Cross-sectional T1-weighted magnetic resonance images of the brain were pooled from 357 participants with chronic stroke from 18 research cohorts of the ENIGMA (Enhancing NeuoImaging Genetics through Meta-Analysis) Stroke Recovery Working Group. Sensorimotor impairment was estimated from the FMA-UE (Fugl-Meyer Assessment of Upper Extremity). Robust mixed-effects linear models were used to test associations between poststroke sensorimotor impairment and hippocampal volumes (ipsilesional and contralesional separately; Bonferroni-corrected, P

Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in stroke research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires neuroanatomical expertise. We previously released an open-source dataset of stroke T1w MRIs and manually-segmented lesion masks (ATLAS v1.2, N = 304) to encourage the development of better algorithms. However, many methods developed with ATLAS v1.2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the field. Here we present ATLAS v2.0 (N = 1271), a larger dataset of T1w MRIs and manually segmented lesion masks that includes training (n = 655), test (hidden masks, n = 300), and generalizability (hidden MRIs and masks, n = 316) datasets. Algorithm development using this larger sample should lead to more robust solutions; the hidden datasets allow for unbiased performance evaluation via segmentation challenges. We anticipate that ATLAS v2.0 will lead to improved algorithms, facilitating large-scale stroke research.

The goal of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well-powered meta- and mega-analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large-scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided.

Schizophrenia is associated with widespread alterations in subcortical brain structure. While analytic methods have enabled more detailed morphometric characterization, findings are often equivocal. In this meta-analysis, we employed the harmonized ENIGMA shape analysis protocols to collaboratively investigate subcortical brain structure shape differences between individuals with schizophrenia and healthy control participants. The study analyzed data from 2,833 individuals with schizophrenia and 3,929 healthy control participants contributed by 21 worldwide research groups participating in the ENIGMA Schizophrenia Working Group. Harmonized shape analysis protocols were applied to each site's data independently for bilateral hippocampus, amygdala, caudate, accumbens, putamen, pallidum, and thalamus obtained from T1-weighted structural MRI scans. Mass univariate meta-analyses revealed more-concave-than-convex shape differences in the hippocampus, amygdala, accumbens, and thalamus in individuals with schizophrenia compared with control participants, more-convex-than-concave shape differences in the putamen and pallidum, and both concave and convex shape differences in the caudate. Patterns of exaggerated asymmetry were observed across the hippocampus, amygdala, and thalamus in individuals with schizophrenia compared to control participants, while diminished asymmetry encompassed ventral striatum and ventral and dorsal thalamus. Our analyses also revealed that higher chlorpromazine dose equivalents and increased positive symptom levels were associated with patterns of contiguous convex shape differences across multiple subcortical structures. Findings from our shape meta-analysis suggest that common neurobiological mechanisms may contribute to gray matter reduction across multiple subcortical regions, thus enhancing our understanding of the nature of network disorganization in schizophrenia.

Up to two-thirds of stroke survivors experience persistent sensorimotor impairments. Recovery relies on the integrity of spared brain areas to compensate for damaged tissue. Deep grey matter structures play a critical role in the control and regulation of sensorimotor circuits. The goal of this work is to identify associations between volumes of spared subcortical nuclei and sensorimotor behaviour at different timepoints after stroke. We pooled high-resolution T1-weighted MRI brain scans and behavioural data in 828 individuals with unilateral stroke from 28 cohorts worldwide. Cross-sectional analyses using linear mixed-effects models related post-stroke sensorimotor behaviour to non-lesioned subcortical volumes (Bonferroni-corrected, P

Objective22q11.2 deletion syndrome (22q11DS) is among the strongest known genetic risk factors for schizophrenia. Previous studies have reported variable alterations in subcortical brain structures in 22q11DS. To better characterize subcortical alterations in 22q11DS, including modulating effects of clinical and genetic heterogeneity, the authors studied a large multicenter neuroimaging cohort from the ENIGMA 22q11.2 Deletion Syndrome Working Group.MethodsSubcortical structures were measured using harmonized protocols for gross volume and subcortical shape morphometry in 533 individuals with 22q11DS and 330 matched healthy control subjects (age range, 6-56 years; 49% female).ResultsCompared with the control group, the 22q11DS group showed lower intracranial volume (ICV) and thalamus, putamen, hippocampus, and amygdala volumes and greater lateral ventricle, caudate, and accumbens volumes (Cohen's d values, -0.90 to 0.93). Shape analysis revealed complex differences in the 22q11DS group across all structures. The larger A-D deletion was associated with more extensive shape alterations compared with the smaller A-B deletion. Participants with 22q11DS with psychosis showed lower ICV and hippocampus, amygdala, and thalamus volumes (Cohen's d values, -0.91 to 0.53) compared with participants with 22q11DS without psychosis. Shape analysis revealed lower thickness and surface area across subregions of these structures. Compared with subcortical findings from other neuropsychiatric disorders studied by the ENIGMA consortium, significant convergence was observed between participants with 22q11DS with psychosis and participants with schizophrenia, bipolar disorder, major depressive disorder, and obsessive-compulsive disorder.ConclusionsIn the largest neuroimaging study of 22q11DS to date, the authors found widespread alterations to subcortical brain structures, which were affected by deletion size and psychotic illness. Findings indicate significant overlap between 22q11DS-associated psychosis, idiopathic schizophrenia, and other severe neuropsychiatric illnesses.

Brain imaging with diffusion-weighted MRI (dMRI) is sensitive to microstructural white matter (WM) changes associated with brain aging and neurodegeneration. In its third phase, the Alzheimer's Disease Neuroimaging Initiative (ADNI3) is collecting data across multiple sites and scanners using different dMRI acquisition protocols, to better understand disease effects. It is vital to understand when data can be pooled across scanners, and how the choice of dMRI protocol affects the sensitivity of extracted measures to differences in clinical impairment. Here, we analyzed ADNI3 data from 317 participants (mean age: 75.4 ± 7.9 years; 143 men/174 women), who were each scanned at one of 47 sites with one of six dMRI protocols using scanners from three different manufacturers. We computed four standard diffusion tensor imaging (DTI) indices including fractional anisotropy (FADTI) and mean, radial, and axial diffusivity, and one FA index based on the tensor distribution function (FATDF), in 24 bilaterally averaged WM regions of interest. We found that protocol differences significantly affected dMRI indices, in particular FADTI. We ranked the diffusion indices for their strength of association with four clinical assessments. In addition to diagnosis, we evaluated cognitive impairment as indexed by three commonly used screening tools for detecting dementia and AD: the AD Assessment Scale (ADAS-cog), the Mini-Mental State Examination (MMSE), and the Clinical Dementia Rating scale sum-of-boxes (CDR-sob). Using a nested random-effects regression model to account for protocol and site, we found that across all dMRI indices and clinical measures, the hippocampal-cingulum and fornix (crus)/stria terminalis regions most consistently showed strong associations with clinical impairment. Overall, the greatest effect sizes were detected in the hippocampal-cingulum (CGH) and uncinate fasciculus (UNC) for associations between axial or mean diffusivity and CDR-sob. FATDF detected robust widespread associations with clinical measures, while FADTI was the weakest of the five indices for detecting associations. Ultimately, we were able to successfully pool dMRI data from multiple acquisition protocols from ADNI3 and detect consistent and robust associations with clinical impairment and age.

Lesion analyses are critical for drawing insights about stroke injury and recovery, and their importance is underscored by growing efforts to collect and combine stroke neuroimaging data across research sites. However, while there are numerous processing pipelines for neuroimaging data in general, few can be smoothly applied to stroke data due to complications analyzing the lesioned region. As researchers often use their own tools or manual methods for stroke MRI analysis, this could lead to greater errors and difficulty replicating findings over time and across sites. Rigorous analysis protocols and quality control pipelines are thus urgently needed for stroke neuroimaging. To this end, we created the Pipeline for Analyzing Lesions after Stroke (PALS; DOI: https://doi.org/10.5281/zenodo.1266980), a scalable and user-friendly toolbox to facilitate and ensure quality in stroke research specifically using T1-weighted MRIs. The PALS toolbox offers four modules integrated into a single pipeline, including (1) reorientation to radiological convention, (2) lesion correction for healthy white matter voxels, (3) lesion load calculation, and (4) visual quality control. In the present paper, we discuss each module and provide validation and example cases of our toolbox using multi-site data. Importantly, we also show that lesion correction with PALS significantly improves similarity between manual lesion segmentations by different tracers (z = 3.43, p = 0.0018). PALS can be found online at https://github.com/npnl/PALS. Future work will expand the PALS capabilities to include multimodal stroke imaging. We hope PALS will be a useful tool for the stroke neuroimaging community and foster new clinical insights.

PurposeIn diffusion MRI (dMRI), fractional anisotropy derived from the single-tensor model (FADTI ) is the most widely used metric to characterize white matter (WM) microarchitecture, despite known limitations in regions with crossing fibers. Due to time constraints when scanning patients in clinical settings, high angular resolution diffusion imaging acquisition protocols, often used to overcome these limitations, are still rare in clinical population studies. However, the tensor distribution function (TDF) may be used to model multiple underlying fibers by representing the diffusion profile as a probabilistic mixture of tensors.MethodsWe compared the ability of standard FADTI and TDF-derived FA (FATDF ), calculated from a range of dMRI angular resolutions (41, 30, 15, and 7 gradient directions), to profile WM deficits in 251 individuals from the Alzheimer's Disease Neuroimaging Initiative and to detect associations with 1) Alzheimer's disease diagnosis, 2) Clinical Dementia Rating scores, and 3) average hippocampal volume.ResultsAcross angular resolutions and statistical tests, FATDF showed larger effect sizes than FADTI , particularly in regions preferentially affected by Alzheimer's disease, and was less susceptible to crossing fiber anomalies.ConclusionThe TDF "corrected" form of FA may be a more sensitive and accurate alternative to the commonly used FADTI , even in clinical quality dMRI data. Magn Reson Med 78:2322-2333, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Diffusion tensor imaging (DTI) has recently been added to several large-scale studies of Alzheimer's disease (AD), such as the Alzheimer's Disease Neuroimaging Initiative (ADNI), to investigate white matter (WM) abnormalities not detectable on standard anatomical MRI. Disease effects can be widespread, and the profile of WM abnormalities across tracts is still not fully understood. Here we analyzed image-wide measures from DTI fractional anisotropy (FA) maps to classify AD patients (n=43), mild cognitive impairment (n=114) and cognitively healthy elderly controls (n=70). We used voxelwise maps of FA along with averages in WM regions of interest (ROI) to drive a Support Vector Machine. We further used the ReliefF algorithm to select the most discriminative WM voxels for classification. This improved accuracy for all classification tasks by up to 15%. We found several clusters formed by the ReliefF algorithm, highlighting specific pathways affected in AD but not always captured when analyzing ROIs.

Introduction: Subanesthetic ketamine is a rapidly acting antidepressant that has also been found to improve neurocognitive performance in adult patients with treatment resistant depression (TRD). Provisional evidence suggests that ketamine may induce change in hippocampal volume and that larger pre-treatment volumes might be related to positive clinical outcomes. Here, we examine the effects of serial ketamine treatment on hippocampal subfield volumes and relationships between pre-treatment subfield volumes and changes in depressive symptoms and neurocognitive performance. Methods: Patients with TRD (N = 66; 31M/35F; age = 39.5 ± 11.1 years) received four ketamine infusions (0.5 mg/kg) over 2 weeks. Structural MRI scans, the National Institutes of Health Toolbox (NIHT) Cognition Battery, and Hamilton Depression Rating Scale (HDRS) were collected at baseline, 24 h after the first and fourth ketamine infusion, and 5 weeks post-treatment. The same data was collected for 32 age and sex matched healthy controls (HC; 17M/15F; age = 35.03 ± 12.2 years) at one timepoint. Subfield (CA1/CA3/CA4/subiculum/molecular layer/GC-ML-DG) volumes corrected for whole hippocampal volume were compared across time, between treatment remitters/non-remitters, and patients and HCs using linear regression models. Relationships between pre-treatment subfield volumes and clinical and cognitive outcomes were also tested. All analyses included Bonferroni correction. Results: Patients had smaller pre-treatment left CA4 (p = 0.004) and GC.ML. DG (p = 0.004) volumes compared to HC, but subfield volumes remained stable following ketamine treatment (all p > 0.05). Pre-treatment or change in hippocampal subfield volumes over time showed no variation by remission status nor correlated with depressive symptoms (p > 0.05). Pre-treatment left CA4 was negatively correlated with improved processing speed after single (p = 0.0003) and serial ketamine infusion (p = 0.005). Left GC.ML.DG also negatively correlated with improved processing speed after single infusion (p = 0.001). Right pretreatment CA3 positively correlated with changes in list sorting working memory at follow-up (p = 0.0007). Discussion: These results provide new evidence to suggest that hippocampal subfield volumes at baseline may present a biomarker for neurocognitive improvement following ketamine treatment in TRD. In contrast, pre-treatment subfield volumes and changes in subfield volumes showed negligible relationships with ketamine-related improvements in depressive symptoms. [ABSTRACT FROM AUTHOR]

As stroke mortality rates decrease, there has been a surge of effort to study poststroke dementia (PSD) to improve long‐term quality of life for stroke survivors. Hippocampal volume may be an important neuroimaging biomarker in poststroke dementia, as it has been associated with many other forms of dementia. However, studying hippocampal volume using MRI requires hippocampal segmentation. Advances in automated segmentation methods have allowed for studying the hippocampus on a large scale, which is important for robust results in the heterogeneous stroke population. However, most of these automated methods use a single atlas‐based approach and may fail in the presence of severe structural abnormalities common in stroke. Hippodeep, a new convolutional neural network‐based hippocampal segmentation method, does not rely solely on a single atlas‐based approach and thus may be better suited for stroke populations. Here, we compared quality control and the accuracy of segmentations generated by Hippodeep and two well‐accepted hippocampal segmentation methods on stroke MRIs (FreeSurfer 6.0 whole hippocampus and FreeSurfer 6.0 sum of hippocampal subfields). Quality control was performed using a stringent protocol for visual inspection of the segmentations, and accuracy was measured as volumetric correlation with manual segmentations. Hippodeep performed significantly better than both FreeSurfer methods in terms of quality control. All three automated segmentation methods had good correlation with manual segmentations and no one method was significantly more correlated than the others. Overall, this study suggests that both Hippodeep and FreeSurfer may be useful for hippocampal segmentation in stroke rehabilitation research, but Hippodeep may be more robust to stroke lesion anatomy. [ABSTRACT FROM AUTHOR]