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Introduction Repetitive peripheral sensory stimulation (RPSS) followed by 4-hour task-specific training (TST) improves upper limb motor function in subjects with stroke who experience moderate to severe motor upper limb impairments. Here, we compared effects of RPSS vs sham followed by a shorter duration of training in subjects with moderate to severe motor impairments in the chronic phase after stroke. Methods This single-center, randomized, placebo-controlled, parallel-group clinical trial compared effects of 18 sessions of either 1.5 h of active RPSS or sham followed by a supervised session that included 45 min of TST of the paretic upper limb. In both groups, subjects were instructed to perform functional tasks at home, without supervision. The primary outcome measure was the Wolf Motor Function Test (WMFT) after 6 weeks of treatment. Grasp and pinch strength were secondary outcomes. Results In intention-to-treat analysis, WMFT improved significantly in both active and sham groups at 3 and 6 weeks of treatment. Grasp strength improved significantly in the active, but not in the sham group, at 3 and 6 weeks. Pinch strength improved significantly in both groups at 3 weeks, and only in the active group at 6 weeks. Conclusions The between-group difference in changes in WMFT was not statistically significant. Despite the short duration of supervised treatment, WMFT improved significantly in subjects treated with RPSS or sham. These findings are relevant to settings that impose constraints in duration of direct contact between therapists and patients. In addition, RPSS led to significant gains in hand strength. Trial Registry Name: Peripheral Nerve Stimulation and Motor Training in Stroke Clinical Trials.gov identifier: NCT0265878 https://clinicaltrials.gov/ct2/show/NCT02658578

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., In this study, we compared three automated hippocampal segmentation methods in a large stroke population in terms of quality control and segmentation accuracy compared to manual segmentations. While all three methods yielded similar volumes, new convolutional neural network based segmentation method Hippodeep had the lowest method‐wise quality control fail rate, suggesting it may be the most robust to post‐stroke anatomical distortions.

Objective: 22q11.2 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. Methods: Subcortical structures were measured using harmonized protocols for gross volume and subcortical shape morphometry in 533 individualswith 22q11DS and 330matched healthy control subjects (age range, 6-56 years; 49% female). Results: Compared 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, 20.90 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, 20.91 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. Conclusions: In 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.

Special Issue: The ENIGMA Consortium: the first 10 years., 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., Center for Integrated Healthcare, U.S. Department of Veterans Affairs, Grant/Award Number: I01 CX000497; Commonwealth Health Research Board, Grant/Award Number: HRA_POR/2011/100; Conselho Nacional de Desenvolvimento Científico e Tecnológico, Grant/Award Numbers: 478466/2009, 480370/2009; Department of Energy, Labor and Economic Growth, Grant/Award Number: DE-FG02-99ER62764; Forskningsrådet om Hälsa, Arbetsliv och Välfärd, Grant/Award Numbers: K2009-62X-15077-06-3, K2012-61X-15077-09-3, 523-2014-3467, 2009-7053, 2013-2838; Fundação Amazônia Paraense de Amparo à Pesquisa, Grant/Award Numbers: 2009/14891-9, 2010/18672-7, 2012/23796-2, 2013/039; Instituto de Salud Carlos III, Grant/Award Numbers: FIS 00/3095, 01/3129, PI020499, PI060507, PI10/001; National Health and Medical Research Council, Grant/Award Numbers: 1009064, 496682; National Institutes of Health, Grant/Award Numbers: 1RC1MH089257, MH 60722, MH019112, MH064045, MH085096, MH098130, MO1 RR025758, P41RR14075, P50 MH071616, R01 DA053028, R01 EB020062, R01 HD050735, R01 MH056584, R01 MH084803, R01 MH116147, R01 MH117601, R01EB005846, R01EB015611, R01MH074797, R21 MH097196, R21MH097196, R37MH43375, S10 OD023696, T32 AG058507, T32 MH073526, TR000153, U01 MH097435, U24 RR021382A, U24 RR021992, U24 RR025736, U24 RR21992, U24RR021992, U54 EB020403, U54EB020403, UL1 TR000153; National Science Foundation, Grant/Award Numbers: 1636893, 1734853; Norges Forskningsråd, Grant/Award Numbers: 213837, 217776, 223273; Science Foundation Ireland, Grant/Award Numbers: 08/IN.1/B1916, 12/IP/1359; Wellcome Trust, Grant/Award Number: 072894/2/03/Z.

Diffusion-weighted magnetic resonance imaging (dMRI) is sensitive to microstructural changes in the brain that occur with normal aging and Alzheimer’s disease (AD). There is much interest in which dMRI measures are most strongly correlated with clinical measures of AD severity, such as the clinical dementia rating (CDR), and biological processes that may be disrupted in AD, such as brain amyloid load measured using PET. Of these processes, some can be targeted using novel drugs. Since 2016, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) has collected dMRI data from three scanner manufacturers across 58 sites using 7 different protocols that vary in angular resolution, scan duration, and in the number and distribution of diffusion-weighted gradients. Here, we assessed dMRI data from 730 of those individuals (447 cognitively normal controls, 214 with mild cognitive impairment, 69 with dementia; age: 74.1±7.9 years; 381 female/349 male). To harmonize data from different protocols, we applied ComBat, ComBat-GAM, and CovBat to dMRI metrics from 28 white matter regions of interest. We ranked all dMRI metrics in order of the strength of clinically relevant associations, and assessed how this depended on the harmonization methods employed. dMRI metrics were associated with age and clinical impairment, but also with amyloid positivity. All harmonization methods gave comparable results while enabling data integration across multiple scanners and protocols.

Schizophrenia (SZ) is a severe psychiatric illness associated with an elevated risk for developing Alzheimer’s disease (AD). Both SZ and AD have white matter abnormalities and cognitive deficits as core disease features. We hypothesized that aging in SZ patients may be associated with the development of cerebral white matter deficit patterns similar to those observed in AD. We identified and replicated aging-related increases in the similarity between white matter deficit patterns in patients with SZ and AD. The white matter “regional vulnerability index” (RVI) for AD was significantly higher in SZ patients compared with healthy controls in both the independent discovery (Cohen’s d = 0.44, P = 1·10–5, N = 173 patients/230 control) and replication (Cohen’s d = 0.78, P = 9·10–7, N = 122 patients/64 controls) samples. The degree of overlap with the AD deficit pattern was significantly correlated with age in patients (r = .21 and .29, P < .01 in discovery and replication cohorts, respectively) but not in controls. Elevated RVI-AD was significantly associated with cognitive measures in both SZ and AD. Disease and cognitive specificities were also tested in patients with mild cognitive impairment and showed intermediate overlap. SZ and AD have diverse etiologies and clinical courses; our findings suggest that white matter deficits may represent a key intersecting point for these 2 otherwise distinct diseases. Identifying mechanisms underlying this white matter deficit pattern may yield preventative and treatment targets for cognitive deficits in both SZ and AD patients.

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.

The Alzheimer’s Disease Neuroimaging Initiative (phase 3; ADNI3) is collecting multisite diffusion MRI (dMRI) data using protocols optimized for different scanner vendors, including one multi-shell protocol, to better understand disease effects. Here, we analyzed multi-shell scans from 56 ADNI3 participants (age: $74.3 \pm 7.5$ yrs; 17F/49M). We evaluated whether multi-shell dMRI measures computed from neurite orientation dispersion and density imaging (NODDI) and diffusion kurtosis imaging (DKI) differentiated people with mild cognitive impairment from healthy controls with higher sensitivity than standard diffusion tensor imaging (DTI) measures. We also assessed the effects of various multi-shell derived dMRI samples on the sensitivity of DTI measures. While we did not identify large differences in effect sizes among tensor-based, NODDI, or DKI measures, we did detect greater effect sizes from DTI measures estimated using multi-shell data converted to single-shell HARDI compared to those fit using the subset of $48 b=1000s /$mm $^{2}$ volumes, typical of DTI.

Brain imaging with diffusion-weighted MRI (dMRI) is sensitive to microstructural white matter 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 white matter 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 Alzheimer’s disease: the Alzheimer’s Disease 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 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 and uncinate fasciculus 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.

Diffusion-weighted MRI (dMRI) offers a range of measures that are sensitive to brain aging and neurodegeneration. Here we analyzed data from 318 participants (mean age: 75.4±7.9 years; 143 men/175 women) from the third phase of the Alzheimer’s Disease Neuroimaging Initiative (ADNI3), who were each scanned with one of six different diffusion MRI protocols using scanners from three different manufacturers. We computed 4 standard diffusion tensor imaging (DTI) anisotropy and diffusivity indices, and one advanced anisotropy index based on the tensor distribution function (TDF), in 24 white matter regions of interest. Modeling protocol effects, we ranked the diffusion indices for their strength of correlation with 3 standard clinical measures of cognitive impairment: the ADAS-Cog, MMSE, and sum-of-boxes Clinical Dementia Rating. Across all dMRI indices and cognitive measures, the cingulum-hippocampal region and the uncinate showed some of the strongest associations with cognitive impairment; largest effect sizes were detected with axial diffusivity (AxDDTI). While fractional anisotropy (FA) derived from the DTI model was the weakest in detecting associations with cognitive measures, FA derived from the TDF detected widespread, robust associations. Protocol differences affected dMRI indices; however by modeling protocol effects, we were able to pool dMRI data from multiple acquisition protocols and detect consistent associations with cognitive impairment and age. dMRI indices computed from the upgraded scanning protocols in ADNI3 were sensitive to cognitive impairment in brain aging, offering a benchmark to compare to future multi-shell or multi-compartment diffusion indices.

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.

Purpose In 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. Methods We 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. Results Across 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. Conclusion The 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.

Susceptibility genes for psychiatric and neurological disorders - including APOE, BDNF, CLU,CNTNAP2, COMT, DISC1, DTNBP1, ErbB4, HFE, NRG1, NTKR3, and ZNF804A - have been reported to affect white matter (WM) microstructure in the healthy human brain, as assessed through diffusion tensor imaging (DTI). However, effects of single nucleotide polymorphisms (SNPs) in these genes explain only a small fraction of the overall variance and are challenging to detect reliably in single cohort studies. To date, few studies have evaluated the reproducibility of these results. As part of the ENIGMA-DTI consortium, we pooled regional fractional anisotropy (FA) measures for 6,165 subjects (CEU ancestry N=4,458) from 11 cohorts worldwide to evaluate effects of 15 candidate SNPs by examining their associations with WM microstructure. Additive association tests were conducted for each SNP. We used several meta-analytic and mega-analytic designs, and we evaluated regions of interest at multiple granularity levels. The ENIGMA-DTI protocol was able to detect single-cohort findings as originally reported. Even so, in this very large sample, no significant associations remained after multiple-testing correction for the 15 SNPs investigated. Suggestive associations (1.3×10-4 < p < 0.05, uncorrected) were found for BDNF, COMT, and ZNF804A in specific tracts. Meta-and mega-analyses revealed similar findings. Regardless of the approach, the previously reported candidate SNPs did not show significant associations with WM microstructure in this largest genetic study of DTI to date; the negative findings are likely not due to insufficient power. Genome-wide studies, involving large-scale meta-analyses, may help to discover SNPs robustly influencing WM microstructure.

Outcomes after traumatic brain injury (TBI) are variable and only partially predicted by acute injury factors. With rich datasets, we can examine how numerous factors – cognitive scores, acute injury variables, demographic variables, and brain imaging variables – are interrelated and aid in outcome prediction. To help study this rich data, we applied CorEx, a novel method for unsupervised machine learning. CorEx decodes the hierarchical structure, identifying latent causes of dependence in the data. It groups predictor variables based on their joint information and inter-dependence. We examined 21 TBI patients 2-5 months post-injury along with healthy controls; both groups were assessed again 12 months later. Although we were limited in the number of participants, this tool for exploratory analysis found potential relationships between change in cognitive scores over the 12-month period and baseline brain volumes. Certain regional brain volumes measured post-injury could serve as predictors of patient recovery. As future planned analyses will examine greater sample sizes, we hope to perform follow-up statistical analysis of variables identified by CorEx in independent data.

Fractional anisotropy derived from the single-tensor model (FADTI) in diffusion MRI (dMRI) is the most widely used metric to characterize white matter (WM) micro-architecture in disease, despite known limitations in regions with extensive fiber crossing. Due to time constraints and interest in collecting multiple clinical samples and MRI scan types, complex HARDI acquisition protocols are rare in clinical population dMRI studies. Under such constraints, the tensor distribution function (TDF) can still be used to reconstruct multiple underlying fibers by representing the diffusion profile as a probabilistic mixture of tensors. Here we set out to better profile WM deficits in Alzheimer's disease (AD) by comparing the standard FADTI and TDF-derived FA (FATDF) in (1) WM network connectivity and voxel-based analyses of diagnostic differences, and (2) for picking up associations with clinical cognitive ratings and hippocampal volume. Ultimately, the TDF approach may be more sensitive and accurate than corresponding DTI-derived measures.

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.