An empirical analysis of structural neuroimaging profiles in a staging model of depression

We examine structural brain characteristics across three diagnostic categories: at risk for serious mental illness; first-presenting episode and recurrent major depressive disorder (MDD). We investigate whether the three diagnostic groups display a stepwise pattern of brain changes in the cortico-limbic regions. Integrated clinical and neuroimaging data from three large Canadian studies were pooled (total n = 622 participants, aged 12-66 years). Four clinical profiles were used in the classification of a clinical staging model: healthy comparison individuals with no history of depression (HC, n = 240), individuals at high risk for serious mental illness due to the presence of subclinical symptoms (SC, n = 80), first-episode depression (FD, n = 82), and participants with recurrent MDD in a current major depressive episode (RD, n = 220). Whole-brain volumetric measurements were extracted with FreeSurfer 7.1 and examined using three different types of analyses. Hippocampal volume decrease and cortico-limbic thinning were the most informative features for the RD vs HC comparisons. FD vs HC revealed that FD participants were characterized by a focal decrease in cortical thickness and global enlargement in amygdala volumes. Greater total amygdala volumes were significantly associated with earlier onset of illness in the FD but not the RD group. We did not confirm the construct validity of a tested clinical staging model, as a differential pattern of brain alterations was identified across the three diagnostic groups that did not parallel a stepwise clinical staging approach. The pathological processes during early stages of the illness may fundamentally differ from those that occur at later stages with clinical progression.

Evaluating denoising strategies in resting-state functional magnetic resonance in traumatic brain injury (EpiBioS4Rx)

Resting-state functional MRI is increasingly used in the clinical setting and is now included in some diagnostic guidelines for severe brain injury patients. However, to ensure high-quality data, one should mitigate fMRI-related noise typical of this population. Therefore, we aimed to evaluate the ability of different preprocessing strategies to mitigate noise-related signal (i.e., in-scanner movement and physiological noise) in functional connectivity (FC) of traumatic brain injury (TBI) patients. We applied nine commonly used denoising strategies, combined into 17 pipelines, to 88 TBI patients from the Epilepsy Bioinformatics Study for Anti-epileptogenic Therapy clinical trial. Pipelines were evaluated by three quality control (QC) metrics across three exclusion regimes based on the participant's head movement profile. While no pipeline eliminated noise effects on FC, some pipelines exhibited relatively high effectiveness depending on the exclusion regime. Once high-motion participants were excluded, the choice of denoising pipeline becomes secondary - although this strategy leads to substantial data loss. Pipelines combining spike regression with physiological regressors were the best performers, whereas pipelines that used automated data-driven methods performed comparatively worse. In this study, we report the first large-scale evaluation of denoising pipelines aimed at reducing noise-related FC in a clinical population known to be highly susceptible to in-scanner motion and significant anatomical abnormalities. If resting-state functional magnetic resonance is to be a successful clinical technique, it is crucial that procedures mitigating the effect of noise be systematically evaluated in the most challenging populations, such as TBI datasets.

Effects of virtual reality-based motor rehabilitation: a systematic review of fMRI studies

BACKGROUND

The use of virtual reality (VR) as a rehabilitation tool has been shown to induce motor and cognitive improvements in different populations. Functional magnetic resonance imaging (fMRI) has been used to investigate neuroplasticity resulting from these treatments. We hypothesize that VR rehabilitation induces functional improvement and brain changes that can be detected by fMRI.

OBJECTIVE

To systematically review the effects of VR intervention on the cortical reorganization measured by fMRI and associated with functional improvement.

METHODS

We performed a systematic review of studies published between 2005 and 2021. Papers were retrieved from six databases using the following keywords: "motor rehabilitation", "fMRI" and "virtual reality". Case studies, pre-post studies, cross-sectional studies, and randomized controlled trials published were included. Manuscripts were assessed by The NIH Study Quality Assessment Tools to determine their quality.

RESULTS

Twenty-three articles met our eligibility criteria: 18 about VR rehabilitation in stroke and five on other clinical conditions (older adults, cerebral palsy, and Parkinson's disease). Changes in neural patterns of activation and reorganization were revealed in both the ipsilesional and the contralesional hemispheres. Results were located mainly in the primary motor cortex, sensorimotor cortex and supplementary motor area in post-stroke patients in the acute, subacute, and chronic rehabilitation phases, and were associated with functional improvement after VR intervention. Similar effects were observed in older adults and in patients with other neurological diseases with improved performance.

CONCLUSION

Most stroke-related studies showed either restoration to normal or increase of activation patterns or relateralization at/to the ipsilesional hemisphere, with some also reporting a decrease in activity or extent of activation after VR therapy. In general, VR intervention demonstrated evidence of efficacy both in neurological rehabilitation and in performance improvement of older adults, accompanied by fMRI evidence of brain reorganization.

ResectVol: A tool to automatically segment and characterize lacunas in brain images

OBJECTIVE

To assess and validate the performance of a new tool developed for segmenting and characterizing lacunas in postoperative MR images of epilepsy patients.

METHODS

A MATLAB-based pipeline was implemented using SPM12 to produce the 3D mask of the surgical lacuna and estimate its volume. To validate its performance, we compared the manual and automatic lacuna segmentations obtained from 51 MRI scans of epilepsy patients who underwent temporal lobe resections.

RESULTS

The code is consolidated as a tool named ResectVol, which can be run via a graphical user interface or command line. The automatic and manual segmentation comparison resulted in a median Dice similarity coefficient of 0.77 (interquartile range: 0.71-0.81).

SIGNIFICANCE

Epilepsy surgery is the treatment of choice for pharmacoresistant focal epilepsies, and despite the extensive literature on the subject, we still cannot predict surgical outcomes accurately. As the volume and location of the resected tissue are fundamentally relevant to this prediction, researchers commonly perform a manual segmentation of the lacuna, which presents human bias and does not provide detailed information about the structures removed. In this study, we introduce ResectVol, a user-friendly, fully automatic tool to accomplish these tasks. This capability enables more advanced analytical techniques applied to surgical outcomes prediction, such as machine-learning algorithms, by facilitating coregistration of the resected area and preoperative findings with other imaging modalities such as PET, SPECT, and functional MRI ResectVol is freely available at https://www.lniunicamp.com/resectvol.

Brain Damage and Gene Expression Across Hereditary Spastic Paraplegia Subtypes

BACKGROUND

Spinal cord has been considered the main target of damage in hereditary spastic paraplegias (HSPs), but mounting evidence indicates that the brain is also affected. Despite this, little is known about the brain signature of HSPs, in particular regarding stratification for specific genetic subtypes.

OBJECTIVE

We aimed to characterize cerebral and cerebellar damage in five HSP subtypes (9 SPG3A, 27 SPG4, 10 SPG7, 9 SPG8, and 29 SPG11) and to uncover the clinical and gene expression correlates.

METHODS

We obtained high-resolution brain T1 and diffusion tensor image (DTI) datasets in this cross-sectional case-control study (n = 84). The MRICloud, FreeSurfer, and CERES-SUIT pipelines were employed to assess cerebral gray (GM) and white matter (WM) as well as the cerebellum.

RESULTS

Brain abnormalities were found in all but one HSP group (SPG3A), but the patterns were gene-specific: basal ganglia, thalamic, and posterior WM involvement in SPG4; diffuse WM and cerebellar involvement in SPG7; cortical thinning at the motor cortices and pallidal atrophy in SPG8; and widespread GM, WM, and deep cerebellar nuclei damage in SPG11. Abnormal regions in SPG4 and SPG8 matched those with higher SPAST and WASHC5 expression, whereas in SPG7 and SPG11 this concordance was only noticed in the cerebellum.

CONCLUSIONS

Brain damage is a conspicuous feature of HSPs (even for pure subtypes), but the pattern of abnormalities is genotype-specific. Correlation between brain structural damage and gene expression maps is different for autosomal dominant and recessive HSPs, pointing to distinct pathophysiological mechanisms underlying brain damage in these subgroups of the disease. © 2021 International Parkinson and Movement Disorder Society.

Spinal Cord Gray and White Matter Damage in Different Hereditary Spastic Paraplegia Subtypes

BACKGROUND AND PURPOSE

Spinal cord damage is a hallmark of hereditary spastic paraplegias, but it is still not clear whether specific subtypes of the disease have distinctive patterns of spinal cord gray (GM) and white (WM) matter involvement. We compared cervical cross-sectional GM and WM areas in patients with distinct hereditary spastic paraplegia subtypes. We also assessed whether these metrics correlated with clinical parameters.

MATERIALS AND METHODS

We analyzed 37 patients (17 men; mean age, 47.3 [SD, 16.5] years) and 21 healthy controls (7 men; mean age, 42.3 [SD, 13.2] years). There were 7 patients with spastic paraplegia type 3A (SPG3A), 12 with SPG4, 10 with SPG7, and 8 with SPG11. Image acquisition was performed on a 3T MR imaging scanner, and T2*-weighted 2D images were assessed by the Spinal Cord Toolbox. Statistical analyses were performed in SPSS using nonparametric tests and false discovery rate-corrected P values < .05.

RESULTS

The mean disease duration for the hereditary spastic paraplegia group was 22.4 [SD, 13.8] years and the mean Spastic Paraplegia Rating Scale score was 22.8 [SD, 11.0]. We failed to identify spinal cord atrophy in SPG3A and SPG7. In contrast, we found abnormalities in patients with SPG4 and SPG11. Both subtypes had spinal cord GM and WM atrophy. SPG4 showed a strong inverse correlation between GM area and disease duration (ρ = -0.903, P < .001).

CONCLUSIONS

Cervical spinal cord atrophy is found in some but not all hereditary spastic paraplegia subtypes. Spinal cord damage in SPG4 and 11 involves both GM and WM.

Alzheimer's Disease Detection Through Whole-Brain 3D-CNN MRI

The projected burden of dementia by Alzheimer's disease (AD) represents a looming healthcare crisis as the population of most countries grows older. Although there is currently no cure, it is possible to treat symptoms of dementia. Early diagnosis is paramount to the development and success of interventions, and neuroimaging represents one of the most promising areas for early detection of AD. We aimed to deploy advanced deep learning methods to determine whether they can extract useful AD biomarkers from structural magnetic resonance imaging (sMRI) and classify brain images into AD, mild cognitive impairment (MCI), and cognitively normal (CN) groups. We tailored and trained Convolutional Neural Networks (CNNs) on sMRIs of the brain from datasets available in online databases. Our proposed method, ADNet, was evaluated on the CADDementia challenge and outperformed several approaches in the prior art. The method's configuration with machine-learning domain adaptation, ADNet-DA, reached 52.3% accuracy. Contributions of our study include devising a deep learning system that is entirely automatic and comparatively fast, presenting competitive results without using any patient's domain-specific knowledge about the disease. We were able to implement an end-to-end CNN system to classify subjects into AD, MCI, or CN groups, reflecting the identification of distinctive elements in brain images. In this context, our system represents a promising tool in finding biomarkers to help with the diagnosis of AD and, eventually, many other diseases.

Interactions between in vivo neuronal-glial markers, side of hippocampal sclerosis, and pharmacoresponse in temporal lobe epilepsy

OBJECTIVE

To evaluate the interactions of metabolic neuronal-glial changes with the presence and hemispheric-side of hippocampal sclerosis (HS) and its potential role in predicting pharmacoresistance in temporal lobe epilepsy (TLE).

METHODS

We included structural magnetic resonance imaging (MRI) and proton magnetic resonance spectroscopy (¹ H-MRS) metabolic data for 91 patients with unilateral TLE and 50 healthy controls. We measured the values of total N-acetyl aspartate/total creatine (tNAA/tCr), glutamate/tCr (Glu/tCr), and myo-inositol/tCr (mIns/tCr). To assess the influence of the pharmacoresponse and hemispheric-side of HS on metabolic data, the relationship between clinical and MRI data, and the predictive value of NAA/Cr, we used analysis of variance/covariance and built a logistic regression model. We used bootstrap simulations to evaluate reproducibility.

RESULTS

Bilateral tNAA/tCr reduction was associated with pharmacoresistance and with left HS, a decrease of Glu/tCr ipsilateral to the seizure focus was associated with pharmacoresistance, and ipsilateral mIns/tCr increase was related to pharmacoresistance and the presence of left HS. The logistic regression model containing clinical and ¹ H-MRS data discriminated pharmacoresistance (area under the curve [AUC] = 0.78). However, the reduction of tNAA/tCr was the main predictor, with the odds 2.48 greater for pharmacoresistance.

SIGNIFICANCE

Our study revealed a spectrum of neuronal-glial changes in TLE, which was associated with pharmacoresistance, being more severe in left-sided HS and less severe in MRI-negative TLE. These noninvasive, in vivo biomarkers provide valuable additional information about the interhemispheric differences in metabolic dysfunction, seizure burden, and HS, and may help to predict pharmacoresistance.

Brain signature of mild stages of cognitive and behavioral impairment in amyotrophic lateral sclerosis

We aimed to assess the brain signature of cognitive and behavioral impairment in C9orf72-negative non-demented ALS patients. The study included 50 amyotrophic lateral sclerosis (ALS) patients (out of 75 initially recruited) and 38 healthy controls. High-resolution T1-weighted and spin-echo diffusion tensor images were acquired in a 3T MRI scanner. The multi atlas-based analysis protocol and the FreeSurfer tool were employed for gray matter assessment, and fiber tractography for white matter evaluation. Cognitively impaired ALS patients (n = 12) had bilateral amygdalae and left thalamic volumetric reduction compared to non-impaired ALS patients. Behaviorally impaired ALS patients (n = 14) had lower fractional anisotropy (FA) at the fornix in comparison with healthy subjects. These parameters did correlate with cognitive/behavioral scores, but not with motor-functional parameters in the ALS cohort. We believe that basal ganglia and fornix damage might be related to cognitive and behavioral impairment across ALS-frontotemporal dementia continuum. Also, distinct anatomical areas seem to influence the behavioral and cognitive status of these individuals.

Derek Denny-Brown: the man behind the ganglia

ABSTRACT The authors present an historical review about the main contributions of Professor Derek Denny-Brown to neurology. Some of his achievements include the first description of sensory neuronopathies, and some of the essential textbooks on the function and anatomy of the basal ganglia. In 2016, on the 35th anniversary of his death, modern neurologists are still strongly influenced by his legacy.

Multimodal MRI-based study in patients with SPG4 mutations

Mutations in the SPG4 gene (SPG4-HSP) are the most frequent cause of hereditary spastic paraplegia, but the extent of the neurodegeneration related to the disease is not yet known. Therefore, our objective is to identify regions of the central nervous system damaged in patients with SPG4-HSP using a multi-modal neuroimaging approach. In addition, we aimed to identify possible clinical correlates of such damage. Eleven patients (mean age 46.0 ± 15.0 years, 8 men) with molecular confirmation of hereditary spastic paraplegia, and 23 matched healthy controls (mean age 51.4 ± 14.1years, 17 men) underwent MRI scans in a 3T scanner. We used 3D T1 images to perform volumetric measurements of the brain and spinal cord. We then performed tract-based spatial statistics and tractography analyses of diffusion tensor images to assess microstructural integrity of white matter tracts. Disease severity was quantified with the Spastic Paraplegia Rating Scale. Correlations were then carried out between MRI metrics and clinical data. Volumetric analyses did not identify macroscopic abnormalities in the brain of hereditary spastic paraplegia patients. In contrast, we found extensive fractional anisotropy reduction in the corticospinal tracts, cingulate gyri and splenium of the corpus callosum. Spinal cord morphometry identified atrophy without flattening in the group of patients with hereditary spastic paraplegia. Fractional anisotropy of the corpus callosum and pyramidal tracts did correlate with disease severity. Hereditary spastic paraplegia is characterized by relative sparing of the cortical mantle and remarkable damage to the distal portions of the corticospinal tracts, extending into the spinal cord.

Thalamic metabolic abnormalities in patients with Huntington's disease measured by magnetic resonance spectroscopy

Huntington's disease (HD) is a neurologic disorder that is not completely understood; its fundamental physiological mechanisms and chemical effects remain somewhat unclear. Among these uncertainties, we can highlight information about the concentrations of brain metabolites, which have been widely discussed. Concentration differences in affected, compared to healthy, individuals could lead to the development of useful tools for evaluating the progression of disease, or to the advance of investigations of different/alternative treatments. The aim of this study was to compare the thalamic concentration of metabolites in HD patients and healthy individuals using magnetic resonance spectroscopy. We used a 2.0-Tesla magnetic field, repetition time of 1500 ms, and echo time of 135 ms. Spectra from 40 adult HD patients and 26 control subjects were compared. Quantitative analysis was performed using the LCModel method. There were statistically significant differences between HD patients and controls in the concentrations of N-acetylaspartate+N-acetylaspartylglutamate (NAA+NAAG; t-test, P<0.001), and glycerophosphocholine+phosphocholine (GPC+PCh; t-test, P=0.001) relative to creatine+phosphocreatine (Cr+PCr). The NAA+NAAG/Cr+PCr ratio was decreased by 9% and GPC+PCh/Cr+PCr increased by 17% in patients compared with controls. There were no correlations between the concentration ratios and clinical features. Although these results could be caused by T1 and T2 changes, rather than variations in metabolite concentrations given the short repetition time and long echo time values used, our findings point to thalamic dysfunction, corroborating prior evidence.