People diagnosed with Parkinson's disease (PD) can experience significant neuropsychiatric symptoms, including cognitive impairment and dementia, the neuroanatomical substrates of which are not fully ...characterised. Symptoms associated with cognitive impairment and dementia in PD may relate to direct structural changes to the corpus callosum via primary white matter pathology or as a secondary outcome due to the degeneration of cortical regions. Using magnetic resonance imaging, the corpus callosum can be investigated at the midsagittal plane, where it converges to a contiguous mass and is not intertwined with other tracts. The objective of this project was thus twofold: First, we investigated possible changes in the thickness of the midsagittal callosum and cortex in patients with PD with varying levels of cognitive impairment; and secondly, we investigated the relationship between the thickness of the midsagittal corpus callosum and the thickness of the cortex. Study participants included cognitively unimpaired PD participants (n = 35), PD participants with mild cognitive impairment (n = 22), PD participants with dementia (n = 17) and healthy controls (n = 27). We found thinning of the callosum in PD‐related dementia compared with PD‐related mild cognitive impairment and cognitively unimpaired PD participants. Regression analyses found thickness of the left medial orbitofrontal cortex to be positively correlated with thickness of the anterior callosum in PD‐related mild cognitive impairment. This study suggests that a midsagittal thickness model can uncover changes to the corpus callosum in PD‐related dementia, which occur in line with changes to the cortex in this advanced disease stage.
Objective
The intricate neuroanatomical structure of the cerebellum is of longstanding interest in epilepsy, but has been poorly characterized within the current corticocentric models of this ...disease. We quantified cross‐sectional regional cerebellar lobule volumes using structural magnetic resonance imaging in 1602 adults with epilepsy and 1022 healthy controls across 22 sites from the global ENIGMA‐Epilepsy working group.
Methods
A state‐of‐the‐art deep learning‐based approach was employed that parcellates the cerebellum into 28 neuroanatomical subregions. Linear mixed models compared total and regional cerebellar volume in (1) all epilepsies, (2) temporal lobe epilepsy with hippocampal sclerosis (TLE‐HS), (3) nonlesional temporal lobe epilepsy, (4) genetic generalized epilepsy, and (5) extratemporal focal epilepsy (ETLE). Relationships were examined for cerebellar volume versus age at seizure onset, duration of epilepsy, phenytoin treatment, and cerebral cortical thickness.
Results
Across all epilepsies, reduced total cerebellar volume was observed (d = .42). Maximum volume loss was observed in the corpus medullare (dmax = .49) and posterior lobe gray matter regions, including bilateral lobules VIIB (dmax = .47), crus I/II (dmax = .39), VIIIA (dmax = .45), and VIIIB (dmax = .40). Earlier age at seizure onset (ηρmax2 = .05) and longer epilepsy duration (ηρmax2 = .06) correlated with reduced volume in these regions. Findings were most pronounced in TLE‐HS and ETLE, with distinct neuroanatomical profiles observed in the posterior lobe. Phenytoin treatment was associated with reduced posterior lobe volume. Cerebellum volume correlated with cerebral cortical thinning more strongly in the epilepsy cohort than in controls.
Significance
We provide robust evidence of deep cerebellar and posterior lobe subregional gray matter volume loss in patients with chronic epilepsy. Volume loss was maximal for posterior subregions implicated in nonmotor functions, relative to motor regions of both the anterior and posterior lobe. Associations between cerebral and cerebellar changes, and variability of neuroanatomical profiles across epilepsy syndromes argue for more precise incorporation of cerebellar subregional damage into neurobiological models of epilepsy.
Parkinson's disease (PD) affects 2-3% of the population over the age of 65 with loss of dopaminergic neurons in the substantia nigra impacting the functioning of basal ganglia-thalamocortical ...circuits. The precise role played by the thalamus is unknown, despite its critical role in the functioning of the cerebral cortex, and the abnormal neuronal activity of the structure in PD. Our objective was to more clearly elucidate how functional connectivity and morphology of the thalamus are impacted in PD (n = 32) compared to Controls (n = 20). To investigate functional connectivity of the thalamus we subdivided the structure into two important regions-of-interest, the first with putative connections to the motor cortices and the second with putative connections to prefrontal cortices. We then investigated potential differences in the size and shape of the thalamus in PD, and how morphology and functional connectivity relate to clinical variables. Our data demonstrate that PD is associated with increases in functional connectivity between motor subdivisions of the thalamus and the supplementary motor area, and between prefrontal thalamic subdivisions and nuclei of the basal ganglia, anterior and dorsolateral prefrontal cortices, as well as the anterior and paracingulate gyri. These results suggest that PD is associated with increased functional connectivity of subdivisions of the thalamus which may be indicative alterations to basal ganglia-thalamocortical circuitry.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
We sought to investigate morphological and resting state functional connectivity changes to the striatal nuclei in Parkinson disease (PD) and examine whether changes were associated with measures of ...clinical function. Striatal nuclei were manually segmented on 3T-T1 weighted MRI scans of 74 PD participants and 27 control subjects, quantitatively analysed for volume, shape and also functional connectivity using functional MRI data. Bilateral caudate nuclei and putamen volumes were significantly reduced in the PD cohort compared to controls. When looking at left and right hemispheres, the PD cohort had significantly smaller left caudate nucleus and right putamen volumes compared to controls. A significant correlation was found between greater atrophy of the caudate nucleus and poorer cognitive function, and between greater atrophy of the putamen and more severe motor symptoms. Resting-state functional MRI analysis revealed altered functional connectivity of the striatal structures in the PD group. This research demonstrates that PD involves atrophic changes to the caudate nucleus and putamen that are linked to clinical dysfunction. Our work reveals important information about a key structure-function relationship in the brain and provides support for caudate nucleus and putamen atrophy as neuroimaging biomeasures in PD.
•Parkinson disease is associated with atrophic changes to the striatal nuclei.•Atrophy of the caudate nucleus correlates with poorer perceptual and cognitive speed.•Atrophy of the putamen correlates with poorer motor symptoms.•Both nuclei show altered resting-state functional connectivity in Parkinson disease.•Striatal nuclei play a crucial role in the symptomology of Parkinson disease.
Background
A practical screening tool to detect Alzheimer’s disease (AD) based on brain MRI would be valuable. Here we tested a deep learning method for subject‐wise AD classification; as gray matter ...(GM) is preferentially affected by AD, we also performed MRI tissue classification on the input data, to test the added value of these input features. We set out to compare different types of imaging data types1 as inputs to a 3D Convolutional Neural Network (CNN) for the AD classification task.
Method
We analyzed T1‐weighted brain MRI scans from 1123 subjects (596M/527F, 55.2 ‐ 95.8 years) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). After registering the T1‐w MRI scans to a common brain template, GM was segmented as shown in Figure 1. We subdivided the dataset into training (3,302 scans/853 subjects), validation (413 scans/100 subjects) and test (170 scans/170 subjects) data for the CNN2. We also trained the CNN on different proportions (10%, 20%, 50% and 100%) of the overall ADNI training data. We further validated our results independently on 232 scans/232 subjects (78M/154F, 33‐96 years) from the Open Access Series of Imaging Studies (OASIS) dataset.
Result
We summarize our results in Table 1 in terms of the receiver‐operator characteristic curve‐area under the curve (ROC‐AUC). Results were aggregated over two separate runs to demonstrate the model’s stability. The CNN with GM segmented T1‐w MRI as input achieved an Average ROC‐AUC of 0.864, compared to 0.859 with the complete T1‐w MRI. The CNN performs better with gray matter segmented input; this performance boost was more pronounced with smaller training sets, i.e., 10% (330 scans) or 20% (660 scans) as seen in Figure 2. The models trained on gray matter maps from ADNI also yielded better performance on the OASIS test set.
Conclusion
In this work, we found that using GM extracted from T1‐w MRI scans improves deep learning‐based AD diagnosis. Feature selection is improved by regulating the data input into the CNN.
1 Lu, B., et al., “A Practical Alzheimer’s Disease Classifier… on 85,721 Samples,” bioRxiv Prepr. (2021).
2 Dhinagar, N. J., et al., “3D CNNs for Classification of Alzheimer’s … with T1‐Weighted Brain MRI,” SIPAIM (2021).
Background
Alzheimer’s disease (AD) is the most common neurodegenerative disorder and apolipoprotein E (APOE) ε4 allele is the greatest common genetic risk factor for late‐onset AD. In one of the ...largest multi‐cohort VBM analyses to date, we aimed to a) map regional brain cortical volume deficits in people with dementia vs controls (CTL) and b) investigate how atrophy patterns in AD were modulated by carrying the APOE4 genotype.
Method
The ENIGMA voxel based morphometry (VBM; https://sites.google.com/view/enigmavbm) pipeline was used to perform a mega‐analysis and a multi‐cohort meta‐analysis on T1‐weighted brain MRI data from 1,893 subjects (Table 1) across four different cohorts, namely the Alzheimer’s Disease Neuroimaging Initiative (ADNI), phases 1, GO/2, 3, and the Open Access Series of Imaging Studies (OASIS3). 488 participants with AD had APOE genotype data with 172 non‐carriers (ε3/3) and 316 carriers (ε3/4, ε4/4). Protective APOE ε2 carriers were excluded. Within the 488 participants with AD, effects of carrying the APOE4 allele were tested using a VBM analysis adjusting for age, sex, intracranial volumes and differences in cohorts.
Result
As expected, compared to controls (CTL), participants with dementia demonstrated cortical brain volume deficits in medial temporal lobes, notably the bilateral hippocampus, entorhinal cortex, amygdala and fusiform gyrus in Mega‐ & Meta‐analysis (Fig. 1). In those with AD, APOE4 carriers demonstrated relatively higher gray matter volumes in the primary motor cortex, medial frontal gyrus and posterior middle temporal gyrus, compared to non‐carriers (P<0.001, uncorrected; Fig. 2). As expected, APOE ε4 carriers demonstrated lower medial temporal lobe and precuneal gray matter volumes.
Conclusion
An expected strong pattern of medial temporal lobe volume reduction was noted in the dementia group compared to CTL. Interestingly, within the dementia group, APOE ε4 carriers showed higher gray matter density in the primary motor cortex, a region not routinely associated with AD nor with amyloid deposition, as shown in a smaller prior study. APOE ε4 carriers showed typical medial temporal and precuneal volume reductions which commonly show amyloid deposition, consistent with the notion that APOE ε4 mediates atrophy mainly via an amyloid dependent mechanism.
References: 1) Ashburner J, Friston KJ. doi:10.1006/nimg.2000.0582. 2) Liu, Ying. doi:10.1016/j.neuron.2014.11.020. 3) La Joie, Renaud. doi:10.1126/scitranslmed.aau5732. 4) Gutiérrez‐Galve L. doi:10.1159/000258100 et al. 2009;28(5):461‐70.
Background
Tractograms generated from diffusion MRI (dMRI) can be used to evaluate white matter abnormalities in Alzheimer's Disease patients (AD). However, the large quantity of streamlines causes ...problems for downstream analysis. Here we use a variational autoencoder (VAE) with a 1D convolutional layer (ConvVAE) to embed streamlines in a 2D latent space. The generative nature of ConvVAE allows us to apply Euclidean distances directly to embeddings and perform intersubject fiber bundle comparisons.
Method
Multi‐shell dMRI from 141 subjects ‐ 87 cognitively normal controls (CN), 44 with mild cognitive impairment (MCI), 10 with dementia (AD) ‐ from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) were preprocessed using the ADNI3 dMRI protocol. 30 white matter tracts were extracted from whole brain tractograms per subject using DIPY's RecoBundles. A control subject with 51,654 streamlines was used to train the ConvVAE with Evidence lower bound (ELBO) loss. We evaluated results by visualizing 2D embeddings and reconstructed streamlines. K‐nearest neighbor (KNN) clustering with Euclidean distance (k=5) was performed on the control subject in a bundle labeling task, to evaluate the alignment between embeddings and generated bundle labels.
Result
2D embeddings for the control subject used for training and a randomly selected MCI and AD subject are shown in Fig. 1, colored by bundles (top) and hemisphere (bottom). Embeddings for each bundle are aligned between subjects, reflecting the shape, size and orientation of the bundle in the streamline space. Embeddings, streamlines and reconstruction for 4 left hemisphere bundles are plotted in Fig. 2. Linearly interpolated points sampled from the latent space translate into smooth transitions in the streamline space, so Euclidean distance can be used in downstream tasks. The fitted KNN model was evaluated on all other subjects where the weighted accuracy for each group was 80.56% (CN), 78.59% (MCI) and 75.76% (AD).
Conclusion
ConvVAE generates 2D embeddings that preserve bundles' spatial and shape information. It learns a smooth latent space from streamlines, which allows for meaningful decodings from sampled points and can be directly applied to new data. Distance‐based algorithms can be used in downstream tasks, such as bundle labeling and intersubject comparisons.
Background
Mild cognitive impairment (MCI) is an early stage of memory loss or cognitive decline and progresses to Alzheimer’s disease at a rate of about 15% per year. It is important to study the ...effects of MCI on the brain. Here we present a novel along‐tract analysis of microstructural brain metrics computed from diffusion MRI. We extract, map, and visualize the profile of microstructural abnormalities on 3D models of fiber tracts, yielding fine‐scale maps of the effects of MCI.
Methods
We analyzed multi‐shell diffusion‐weighted MRI data with 127 volumes from 131 ADNI3 participants (age: 55‐91years, 74F,57M). Participants included 44 with MCI and 87 cognitively normal controls (CN). Pre‐processed using the ADNI3 dMRI protocol. We applied multi‐shell‐multi‐tissue CSD and a probabilistic particle‐filtering‐tracking to generate whole‐brain tractograms. We applied DIPY’s BUAN tractometry pipeline to extract 30 white matter tracts from all subjects and evaluate the effects of MCI on 4 DTI measures (FA, MD, RD, and AD) along the length of the tracts. BUAN applies Linear‐Mixed‐Models with DTI measures set as the response variable, group mean modeled as fixed effects term, and subject‐specific mean as random effects term.
Results
We report significant group differences between MCI and CN in 6 bundles with p‐values<0.01 and <0.001 (Fig.1). In Fig.1 and Fig2, the columns and rows show results from six bundles for four DTI measures (see Fig.2 for keywords). Fig.2 highlights areas with significant differences on the tracts with black color where for MD, p‐values<0.001 and for the rest of the 3 measures, p‐values<0.01. Fig.3 reports significant effects of MCI on the rest of the tracts and measures with p‐values<0.001. We find lower mean FA and higher mean MD, AD, and RD in the MCI, compared to the control group.
Conclusion
We applied the BUAN tractometry pipeline to map and visualize the effects of MCI on the white matter tracts of the brain. We found significant microstructural group differences in tracts where FA decreases and diffusivity measures increase in MCI participants.
PDF
