Cortico-basal ganglia white matter microstructure is linked to restricted repetitive behavior in autism spectrum disorder.

BACKGROUND: Restricted repetitive behavior (RRB) is one of two behavioral domains required for the diagnosis of autism spectrum disorder (ASD). Neuroimaging is widely used to study brain alterations associated with ASD and the domain of social and communication deficits, but there has been less work regarding brain alterations linked to RRB. METHODS: We utilized neuroimaging data from the National Institute of Mental Health Data Archive to assess basal ganglia and cerebellum structure in a cohort of children and adolescents with ASD compared to typically developing (TD) controls. We evaluated regional gray matter volumes from T1-weighted anatomical scans and assessed diffusion-weighted scans to quantify white matter microstructure with free-water imaging. We also investigated the interaction of biological sex and ASD diagnosis on these measures, and their correlation with clinical scales of RRB. RESULTS: Individuals with ASD had significantly lower free-water corrected fractional anisotropy (FAT) and higher free-water (FW) in cortico-basal ganglia white matter tracts. These microstructural differences did not interact with biological sex. Moreover, both FAT and FW in basal ganglia white matter tracts significantly correlated with measures of RRB. In contrast, we found no significant difference in basal ganglia or cerebellar gray matter volumes. LIMITATIONS: The basal ganglia and cerebellar regions in this study were selected due to their hypothesized relevance to RRB. Differences between ASD and TD individuals that may occur outside the basal ganglia and cerebellum, and their potential relationship to RRB, were not evaluated. CONCLUSIONS: These new findings demonstrate that cortico-basal ganglia white matter microstructure is altered in ASD and linked to RRB. FW in cortico-basal ganglia and intra-basal ganglia white matter was more sensitive to group differences in ASD, whereas cortico-basal ganglia FAT was more closely linked to RRB. In contrast, basal ganglia and cerebellar volumes did not differ in ASD. There was no interaction between ASD diagnosis and sex-related differences in brain structure. Future diffusion imaging investigations in ASD may benefit from free-water estimation and correction in order to better understand how white matter is affected in ASD, and how such measures are linked to RRB.

Sex-specific genetic architecture of late-life memory performance.

BACKGROUND: Women demonstrate a memory advantage when cognitively healthy yet lose this advantage to men in Alzheimer's disease. However, the genetic underpinnings of this sex difference in memory performance remain unclear. METHODS: We conducted the largest sex-aware genetic study on late-life memory to date (Nmales  = 11,942; Nfemales  = 15,641). Leveraging harmonized memory composite scores from four cohorts of cognitive aging and AD, we performed sex-stratified and sex-interaction genome-wide association studies in 24,216 non-Hispanic White and 3367 non-Hispanic Black participants. RESULTS: We identified three sex-specific loci (rs67099044-CBLN2, rs719070-SCHIP1/IQCJ-SCHIP), including an X-chromosome locus (rs5935633-EGL6/TCEANC/OFD1), that associated with memory. Additionally, we identified heparan sulfate signaling as a sex-specific pathway and found sex-specific genetic correlations between memory and cardiovascular, immune, and education traits. DISCUSSION: This study showed memory is highly and comparably heritable across sexes, as well as highlighted novel sex-specific genes, pathways, and genetic correlations that related to late-life memory. HIGHLIGHTS: Demonstrated the heritable component of late-life memory is similar across sexes. Identified two genetic loci with a sex-interaction with baseline memory. Identified an X-chromosome locus associated with memory decline in females. Highlighted sex-specific candidate genes and pathways associated with memory. Revealed sex-specific shared genetic architecture between memory and complex traits.

Longitudinal change in memory performance as a strong endophenotype for Alzheimer's disease.

INTRODUCTION: Although large-scale genome-wide association studies (GWAS) have been conducted on AD, few have been conducted on continuous measures of memory performance and memory decline. METHODS: We conducted a cross-ancestry GWAS on memory performance (in 27,633 participants) and memory decline (in 22,365 participants; 129,201 observations) by leveraging harmonized cognitive data from four aging cohorts. RESULTS: We found high heritability for two ancestry backgrounds. Further, we found a novel ancestry locus for memory decline on chromosome 4 (rs6848524) and three loci in the non-Hispanic Black ancestry group for memory performance on chromosomes 2 (rs111471504), 7 (rs4142249), and 15 (rs74381744). In our gene-level analysis, we found novel genes for memory decline on chromosomes 1 (SLC25A44), 11 (BSX), and 15 (DPP8). Memory performance and memory decline shared genetic architecture with AD-related traits, neuropsychiatric traits, and autoimmune traits. DISCUSSION: We discovered several novel loci, genes, and genetic correlations associated with late-life memory performance and decline. HIGHLIGHTS: Late-life memory has high heritability that is similar across ancestries. We discovered four novel variants associated with late-life memory. We identified four novel genes associated with late-life memory. Late-life memory shares genetic architecture with psychiatric/autoimmune traits.

Predicting Age from White Matter Diffusivity with Residual Learning.

Imaging findings inconsistent with those expected at specific chronological age ranges may serve as early indicators of neurological disorders and increased mortality risk. Estimation of chronological age, and deviations from expected results, from structural magnetic resonance imaging (MRI) data has become an important proxy task for developing biomarkers that are sensitive to such deviations. Complementary to structural analysis, diffusion tensor imaging (DTI) has proven effective in identifying age-related microstructural changes within the brain white matter, thereby presenting itself as a promising additional modality for brain age prediction. Although early studies have sought to harness DTI's advantages for age estimation, there is no evidence that the success of this prediction is owed to the unique microstructural and diffusivity features that DTI provides, rather than the macrostructural features that are also available in DTI data. Therefore, we seek to develop white-matter-specific age estimation to capture deviations from normal white matter aging. Specifically, we deliberately disregard the macrostructural information when predicting age from DTI scalar images, using two distinct methods. The first method relies on extracting only microstructural features from regions of interest (ROIs). The second applies 3D residual neural networks (ResNets) to learn features directly from the images, which are non-linearly registered and warped to a template to minimize macrostructural variations. When tested on unseen data, the first method yields mean absolute error (MAE) of 6.11 ± 0.19 years for cognitively normal participants and MAE of 6.62 ± 0.30 years for cognitively impaired participants, while the second method achieves MAE of 4.69 ± 0.23 years for cognitively normal participants and MAE of 4.96 ± 0.28 years for cognitively impaired participants. We find that the ResNet model captures subtler, non-macrostructural features for brain age prediction.

Characterizing patterns of DTI variance in aging brains.

Background: As large analyses merge data across sites, a deeper understanding of variance in statistical assessment across the sources of data becomes critical for valid analyses. Diffusion tensor imaging (DTI) exhibits spatially varying and correlated noise, so care must be taken with distributional assumptions. Purpose: We characterize the role of physiology, subject compliance, and the interaction of subject with the scanner in the understanding of DTI variability, as modeled in spatial variance of derived metrics in homogeneous regions. Methods: We analyze DTI data from 1035 subjects in the Baltimore Longitudinal Study of Aging (BLSA), with ages ranging from 22.4 to 103 years old. For each subject, up to 12 longitudinal sessions were conducted. We assess variance of DTI scalars within regions of interest (ROIs) defined by four segmentation methods and investigate the relationships between the variance and covariates, including baseline age, time from the baseline (referred to as "interval"), motion, sex, and whether it is the first scan or the second scan in the session. Results: Covariate effects are heterogeneous and bilaterally symmetric across ROIs. Inter-session interval is positively related (p ≪ 0.001) to FA variance in the cuneus and occipital gyrus, but negatively (p ≪ 0.001) in the caudate nucleus. Males show significantly (p ≪ 0.001) higher FA variance in the right putamen, thalamus, body of the corpus callosum, and cingulate gyrus. In 62 out of 176 ROIs defined by the Eve type-1 atlas, an increase in motion is associated (p < 0.05) with a decrease in FA variance. Head motion increases during the rescan of DTI (Δµ = 0.045 millimeters per volume). Conclusions: The effects of each covariate on DTI variance, and their relationships across ROIs are complex. Ultimately, we encourage researchers to include estimates of variance when sharing data and consider models of heteroscedasticity in analysis. This work provides a foundation for study planning to account for regional variations in metric variance.

A deep neural network estimation of brain age is sensitive to cognitive impairment and decline.

The greatest known risk factor for Alzheimer's disease (AD) is age. While both normal aging and AD pathology involve structural changes in the brain, their trajectories of atrophy are not the same. Recent developments in artificial intelligence have encouraged studies to leverage neuroimaging-derived measures and deep learning approaches to predict brain age, which has shown promise as a sensitive biomarker in diagnosing and monitoring AD. However, prior efforts primarily involved structural magnetic resonance imaging and conventional diffusion MRI (dMRI) metrics without accounting for partial volume effects. To address this issue, we post-processed our dMRI scans with an advanced free-water (FW) correction technique to compute distinct FW-corrected fractional anisotropy (FAFWcorr) and FW maps that allow for the separation of tissue from fluid in a scan. We built 3 densely connected neural networks from FW-corrected dMRI, T1-weighted MRI, and combined FW+T1 features, respectively, to predict brain age. We then investigated the relationship of actual age and predicted brain ages with cognition. We found that all models accurately predicted actual age in cognitively unimpaired (CU) controls (FW: r=0.66, p=1.62x10-32; T1: r=0.61, p=1.45x10-26, FW+T1: r=0.77, p=6.48x10-50) and distinguished between CU and mild cognitive impairment participants (FW: p=0.006; T1: p=0.048; FW+T1: p=0.003), with FW+T1-derived age showing best performance. Additionally, all predicted brain age models were significantly associated with cross-sectional cognition (memory, FW: ß=-1.094, p=6.32x10-7; T1: ß=-1.331, p=6.52x10-7; FW+T1: ß=-1.476, p=2.53x10-10; executive function, FW: ß=-1.276, p=1.46x10-9; T1: ß=-1.337, p=2.52x10-7; FW+T1: ß=-1.850, p=3.85x10-17) and longitudinal cognition (memory, FW: ß=-0.091, p=4.62x10-11; T1: ß=-0.097, p=1.40x10-8; FW+T1: ß=-0.101, p=1.35x10-11; executive function, FW: ß=-0.125, p=1.20x10-10; T1: ß=-0.163, p=4.25x10-12; FW+T1: ß=-0.158, p=1.65x10-14). Our findings provide evidence that both T1-weighted MRI and dMRI measures improve brain age prediction and support predicted brain age as a sensitive biomarker of cognition and cognitive decline.

Leveraging longitudinal diffusion MRI data to quantify differences in white matter microstructural decline in normal and abnormal aging.

Introduction: It is unclear how rates of white matter microstructural decline differ between normal aging and abnormal aging. Methods: Diffusion MRI data from several well-established longitudinal cohorts of aging (Alzheimer's Disease Neuroimaging Initiative [ADNI], Baltimore Longitudinal Study of Aging [BLSA], Vanderbilt Memory & Aging Project [VMAP]) were free-water corrected and harmonized. This dataset included 1723 participants (age at baseline: 72.8 ± 8.87 years, 49.5% male) and 4605 imaging sessions (follow-up time: 2.97 ± 2.09 years, follow-up range: 1-13 years, mean number of visits: 4.42 ± 1.98). Differences in white matter microstructural decline in normal and abnormal agers was assessed. Results: While we found a global decline in white matter in normal/abnormal aging, we found that several white matter tracts (e.g., cingulum bundle) were vulnerable to abnormal aging. Conclusions: There is a prevalent role of white matter microstructural decline in aging, and future large-scale studies in this area may further refine our understanding of the underlying neurodegenerative processes. HIGHLIGHTS: Longitudinal data were free-water corrected and harmonized.Global effects of white matter decline were seen in normal and abnormal aging.The free-water metric was most vulnerable to abnormal aging.Cingulum free-water was the most vulnerable to abnormal aging.

A deep neural network estimation of brain age is sensitive to cognitive impairment and decline.

The greatest known risk factor for Alzheimer's disease (AD) is age. While both normal aging and AD pathology involve structural changes in the brain, their trajectories of atrophy are not the same. Recent developments in artificial intelligence have encouraged studies to leverage neuroimaging-derived measures and deep learning approaches to predict brain age, which has shown promise as a sensitive biomarker in diagnosing and monitoring AD. However, prior efforts primarily involved structural magnetic resonance imaging and conventional diffusion MRI (dMRI) metrics without accounting for partial volume effects. To address this issue, we post-processed our dMRI scans with an advanced free-water (FW) correction technique to compute distinct FW-corrected fractional anisotropy (FAFWcorr) and FW maps that allow for the separation of tissue from fluid in a scan. We built 3 densely connected neural networks from FW-corrected dMRI, T1-weighted MRI, and combined FW+T1 features, respectively, to predict brain age. We then investigated the relationship of actual age and predicted brain ages with cognition. We found that all models accurately predicted actual age in cognitively unimpaired (CU) controls (FW: r=0.66, p=1.62×10-32; T1: r=0.61, p=1.45×10-26, FW+T1: r=0.77, p=6.48×10-50) and distinguished between CU and mild cognitive impairment participants (FW: p=0.006; T1: p=0.048; FW+T1: p=0.003), with FW+T1-derived age showing best performance. Additionally, all predicted brain age models were significantly associated with cross-sectional cognition (memory, FW: ß=-1.094, p=6.32×10-7; T1: ß=-1.331, p=6.52×10-7; FW+T1: ß=-1.476, p=2.53×10-10; executive function, FW: ß=-1.276, p=1.46×10-9; T1: ß=-1.337, p=2.52×10-7; FW+T1: ß=-1.850, p=3.85×10-17) and longitudinal cognition (memory, FW: ß=-0.091, p=4.62×10-11; T1: ß=-0.097, p=1.40×10-8; FW+T1: ß=-0.101, p=1.35×10-11; executive function, FW: ß=-0.125, p=1.20×10-10; T1: ß=-0.163, p=4.25×10-12; FW+T1: ß=-0.158, p=1.65×10-14). Our findings provide evidence that both T1-weighted MRI and dMRI measures improve brain age prediction and support predicted brain age as a sensitive biomarker of cognition and cognitive decline.

Leveraging longitudinal diffusion MRI data to quantify differences in white matter microstructural decline in normal and abnormal aging.

INTRODUCTION: It is unclear how rates of white matter microstructural decline differ between normal aging and abnormal aging. METHODS: Diffusion MRI data from several well-established longitudinal cohorts of aging [Alzheimer's Neuroimaging Initiative (ADNI), Baltimore Longitudinal Study of Aging (BLSA), Vanderbilt Memory & Aging Project (VMAP)] was free-water corrected and harmonized. This dataset included 1,723 participants (age at baseline: 72.8±8.87 years, 49.5% male) and 4,605 imaging sessions (follow-up time: 2.97±2.09 years, follow-up range: 1-13 years, mean number of visits: 4.42±1.98). Differences in white matter microstructural decline in normal and abnormal agers was assessed. RESULTS: While we found global decline in white matter in normal/abnormal aging, we found that several white matter tracts (e.g., cingulum bundle) were vulnerable to abnormal aging. CONCLUSIONS: There is a prevalent role of white matter microstructural decline in aging, and future large-scale studies in this area may further refine our understanding of the underlying neurodegenerative processes. HIGHLIGHTS: Longitudinal data was free-water corrected and harmonizedGlobal effects of white matter decline were seen in normal and abnormal agingThe free-water metric was most vulnerable to abnormal agingCingulum free-water was the most vulnerable to abnormal aging.

White matter microstructural metrics are sensitively associated with clinical staging in Alzheimer's disease.

Introduction: White matter microstructure may be abnormal along the Alzheimer's disease (AD) continuum. Methods: Diffusion magnetic resonance imaging (dMRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 627), Baltimore Longitudinal Study of Aging (BLSA, n = 684), and Vanderbilt Memory & Aging Project (VMAP, n = 296) cohorts were free-water (FW) corrected and conventional, and FW-corrected microstructural metrics were quantified within 48 white matter tracts. Microstructural values were subsequently harmonized using the Longitudinal ComBat technique and inputted as independent variables to predict diagnosis (cognitively unimpaired [CU], mild cognitive impairment [MCI], AD). Models were adjusted for age, sex, race/ethnicity, education, apolipoprotein E (APOE) ε4 carrier status, and APOE ε2 carrier status. Results: Conventional dMRI metrics were associated globally with diagnostic status; following FW correction, the FW metric itself exhibited global associations with diagnostic status, but intracellular metric associations were diminished. Discussion: White matter microstructure is altered along the AD continuum. FW correction may provide further understanding of the white matter neurodegenerative process in AD. Highlights: Longitudinal ComBat successfully harmonized large-scale diffusion magnetic resonance imaging (dMRI) metrics.Conventional dMRI metrics were globally sensitive to diagnostic status.Free-water (FW) correction mitigated intracellular associations with diagnostic status.The FW metric itself was globally sensitive to diagnostic status. Multivariate conventional and FW-corrected models may provide complementary information.

Large multi-ethnic genetic analyses of amyloid imaging identify new genes for Alzheimer disease.

Amyloid PET imaging has been crucial for detecting the accumulation of amyloid beta (Aß) deposits in the brain and to study Alzheimer's disease (AD). We performed a genome-wide association study on the largest collection of amyloid imaging data (N = 13,409) to date, across multiple ethnicities from multicenter cohorts to identify variants associated with brain amyloidosis and AD risk. We found a strong APOE signal on chr19q.13.32 (top SNP: APOE ɛ4; rs429358; ß = 0.35, SE = 0.01, P = 6.2 × 10-311, MAF = 0.19), driven by APOE ɛ4, and five additional novel associations (APOE ε2/rs7412; rs73052335/rs5117, rs1081105, rs438811, and rs4420638) independent of APOE ɛ4. APOE ɛ4 and ε2 showed race specific effect with stronger association in Non-Hispanic Whites, with the lowest association in Asians. Besides the APOE, we also identified three other genome-wide loci: ABCA7 (rs12151021/chr19p.13.3; ß = 0.07, SE = 0.01, P = 9.2 × 10-09, MAF = 0.32), CR1 (rs6656401/chr1q.32.2; ß = 0.1, SE = 0.02, P = 2.4 × 10-10, MAF = 0.18) and FERMT2 locus (rs117834516/chr14q.22.1; ß = 0.16, SE = 0.03, P = 1.1 × 10-09, MAF = 0.06) that all colocalized with AD risk. Sex-stratified analyses identified two novel female-specific signals on chr5p.14.1 (rs529007143, ß = 0.79, SE = 0.14, P = 1.4 × 10-08, MAF = 0.006, sex-interaction P = 9.8 × 10-07) and chr11p.15.2 (rs192346166, ß = 0.94, SE = 0.17, P = 3.7 × 10-08, MAF = 0.004, sex-interaction P = 1.3 × 10-03). We also demonstrated that the overall genetic architecture of brain amyloidosis overlaps with that of AD, Frontotemporal Dementia, stroke, and brain structure-related complex human traits. Overall, our results have important implications when estimating the individual risk to a population level, as race and sex will needed to be taken into account. This may affect participant selection for future clinical trials and therapies.

Associations among executive function Abilities, free Water, and white matter microstructure in early old age.

BACKGROUND: Studies have investigated white matter microstructure in relation to late-life cognitive impairments, with fractional anisotropy (FA) and mean diffusivity (MD) measures thought to capture demyelination and axonal degradation. However, new post-processing methods allow isolation of free water (FW), which captures extracellular fluid contributions such as atrophy and neuroinflammation, from tissue components. FW also appears to be highly relevant to late-life cognitive impairment. Here, we evaluated whether executive functions are associated with FW, and FA and MD corrected for FW (FAFWcorr and MDFWcorr). METHOD: We examined 489 non-demented men in the Vietnam Era Twin Study of Aging (VETSA) at mean age 68. Two latent factors capturing 'common executive function' and 'working-memory specific' processes were estimated based on 6 tasks. Analyses focused on 11 cortical white matter tracts across three metrics: FW, FAFWcorr, and MDFWcorr. RESULTS: Better 'common executive function' was associated with lower FW across 9 of the 11 tracts. There were no significant associations with intracellular metrics after false discovery rate correction. Effects also appeared driven by individuals with MCI (13.7% of the sample). Working memory-specific tasks showed some associations with FAFWcorr, including the triangularis portion of the inferior frontal gyrus. There was no evidence that cognitive reserve (i.e., general cognitive ability assessed in early adulthood) moderated these associations between executive function and FW or FA. DISCUSSION: Executive function abilities in early old age are associated primarily with extracellular fluid (FW) as opposed to white matter (FAFWcorr or MDFWcorr). Moderation analyses suggested cognitive reserve does not play a strong role in these associations, at least in this sample of non-demented men.

Transcallosal and Corticospinal White Matter Disease and Its Association With Motor Impairment in Multiple Sclerosis.

Purpose: In this cross-sectional, proof-of-concept study, we propose that using the more pathologically-specific neurite orientation dispersion and density imaging (NODDI) method, in conjunction with high-resolution probabilistic tractography, white matter tract templates can improve the assessment of regional axonal injury and its association with disability of people with multiple sclerosis (pwMS). Methods: Parametric maps of the neurite density index, orientation dispersion index, and the apparent isotropic volume fraction (IVF) were estimated in 18 pwMS and nine matched healthy controls (HCs). Tract-specific values were measured in transcallosal (TC) fibers from the paracentral lobules and TC and corticospinal fibers from the ventral and dorsal premotor areas, presupplementary and supplementary motor areas, and primary motor cortex. The nonparametric Mann-Whitney U test assessed group differences in the NODDI-derived metrics; the Spearman's rank correlation analyses measured associations between the NODDI metrics and other clinical or radiological variables. Results: IVF values of the TC fiber bundles from the paracentral, presupplementary, and supplementary motor areas were both higher in pwMS than in HCs (p ≤ 0.045) and in pwMS with motor disability compared to those without motor disability (p ≤ 0.049). IVF in several TC tracts was associated with the Expanded Disability Status Scale score (p ≤ 0.047), while regional and overall lesion burden correlated with the Timed 25-Foot Walking Test (p ≤ 0.049). Conclusion: IVF alterations are present in pwMS even when the other NODDI metrics are still mostly preserved. Changes in IVF are biologically non-specific and may not necessarily drive irreversible functional loss. However, by possibly preceding downstream pathologies that are strongly associated with disability accretion, IVF changes are indicators of, otherwise, occult prelesional tissue injury.

Advanced diffusion imaging to track progression in Parkinson's disease, multiple system atrophy, and progressive supranuclear palsy.

Advanced diffusion imaging which accounts for complex tissue properties, such as crossing fibers and extracellular fluid, may detect longitudinal changes in widespread pathology in atypical Parkinsonian syndromes. We implemented fixel-based analysis, Neurite Orientation and Density Imaging (NODDI), and free-water imaging in Parkinson's disease (PD), multiple system atrophy (MSAp), progressive supranuclear palsy (PSP), and controls longitudinally over one year. Further, we used these three advanced diffusion imaging techniques to investigate longitudinal progression-related effects in key white matter tracts and gray matter regions in PD and two common atypical Parkinsonian disorders. Fixel-based analysis and free-water imaging revealed longitudinal declines in a greater number of descending sensorimotor tracts in MSAp and PSP compared to PD. In contrast, only the primary motor descending sensorimotor tract had progressive decline over one year, measured by fiber density (FD), in PD compared to that in controls. PSP was characterized by longitudinal impairment in multiple transcallosal tracts (primary motor, dorsal and ventral premotor, pre-supplementary motor, and supplementary motor area) as measured by FD, whereas there were no transcallosal tracts with longitudinal FD impairment in MSAp and PD. In addition, free-water (FW) and FW-corrected fractional anisotropy (FAt) in gray matter regions showed longitudinal changes over one year in regions that have previously shown cross-sectional impairment in MSAp (putamen) and PSP (substantia nigra, putamen, subthalamic nucleus, red nucleus, and pedunculopontine nucleus). NODDI did not detect any longitudinal white matter tract progression effects and there were few effects in gray matter regions across Parkinsonian disorders. All three imaging methods were associated with change in clinical disease severity across all three Parkinsonian syndromes. These results identify novel extra-nigral and extra-striatal longitudinal progression effects in atypical Parkinsonian disorders through the application of multiple diffusion methods that are related to clinical disease progression. Moreover, the findings suggest that fixel-based analysis and free-water imaging are both particularly sensitive to these longitudinal changes in atypical Parkinsonian disorders.

Diffusion Magnetic Resonance Imaging Detects Progression in Parkinson's Disease: A Placebo-Controlled Trial of Rasagiline.

BACKGROUND: Rasagiline has received attention as a potential disease-modifying therapy for Parkinson's disease (PD). Whether rasagiline is disease modifying remains in question. OBJECTIVE: The main objective of this study was to determine whether rasagiline has disease-modifying effects in PD over 1 year. Secondarily we evaluated two diffusion magnetic resonance imaging pulse sequences to determine the best sequence to measure disease progression. METHODS: This prospective, randomized, double-blind, placebo-controlled trial assessed the effects of rasagiline administered at 1 mg/day over 12 months in early-stage PD. The primary outcome was 1-year change in free-water accumulation in posterior substantia nigra (pSN) measured using two diffusion magnetic resonance imaging pulse sequences, one with a repetition time (TR) of 2500 ms (short TR; n = 90) and one with a TR of 6400 ms (long TR; n = 75). Secondary clinical outcomes also were assessed. RESULTS: Absolute change in pSN free-water accumulation was not significantly different between groups (short TR: P = 0.346; long TR: P = 0.228). No significant differences were found in any secondary clinical outcomes between groups. Long TR, but not short TR, data show pSN free-water increased significantly over 1 year (P = 0.025). Movement Disorder Society Unified Parkinson's Disease Rating Scale testing of motor function, Part III increased significantly over 1 year (P = 0.009), and baseline free-water in the pSN correlated with the 1-year change in Movement Disorder Society Unified Parkinson's Disease Rating Scale testing of motor function, Part III (P = 0.004) and 1-year change in bradykinesia score (P = 0.044). CONCLUSIONS: We found no evidence that 1 mg/day rasagiline has a disease-modifying effect in PD over 1 year. We found pSN free-water increased over 1 year, and baseline free-water relates to clinical motor progression, demonstrating the importance of diffusion imaging parameters for detecting and predicting PD progression. © 2021 International Parkinson and Movement Disorder Society.

The relationship between white matter microstructure and self-perceived cognitive decline.

Subjective cognitive decline (SCD) is a perceived cognitive change prior to objective cognitive deficits, and although it is associated with Alzheimer's disease (AD) pathology, it likely results from multiple underlying pathologies. We investigated the association of white matter microstructure to SCD as a sensitive and early marker of cognitive decline and quantified the contribution of white matter microstructure separate from amyloidosis. Vanderbilt Memory & Aging Project participants with diffusion MRI data and a 45-item measure of SCD were included [n = 236, 137 cognitively unimpaired (CU), 99 with mild cognitive impairment (MCI), 73 ± 7 years, 37% female]. A subset of participants (64 CU, 40 MCI) underwent a fasting lumbar puncture for quantification of cerebrospinal fluid (CSF) amyloid-ß(CSF Aß42), total tau (CSF t-tau), and phosphorylated tau (CSF p-tau). Diffusion MRI data was post-processed using the free-water (FW) elimination technique, which allowed quantification of extracellular (FW) and intracellular compartment (fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity) microstructure. Microstructural values were quantified within 11 cognitive-related white matter tracts, including medial temporal lobe, frontal transcallosal, and fronto-parietal tracts using a region of interest approach. General linear modeling related each tract to SCD scores adjusting for age, sex, race/ethnicity, education, Framingham Stroke Risk Profile scores, APOE ε4 carrier status, diagnosis, Geriatric Depression Scale scores, hippocampal volume, and total white matter volume. Competitive models were analyzed to determine if white matter microstructural values have a unique role in SCD scores separate from CSF Aß42. FW-corrected radial diffusivity (RDT) was related to SCD scores in 8 tracts: cingulum bundle, inferior longitudinal fasciculus, as well as inferior frontal gyrus (IFG) pars opercularis, IFG orbitalis, IFG pars triangularis, tapetum, medial frontal gyrus, and middle frontal gyrus transcallosal tracts. While CSF Aß42 was related to SCD scores in our cohort (Radj2 = 39.03%; ß = -0.231; p = 0.020), competitive models revealed that fornix and IFG pars triangularis transcallosal tract RDT contributed unique variance to SCD scores beyond CSF Aß42 (Radj2 = 44.35% and Radj2 = 43.09%, respectively), with several other tract measures demonstrating nominal significance. All tracts which demonstrated nominal significance (in addition to covariates) were input into a backwards stepwise regression analysis. ILF RDT, fornix RDT, and UF FW were best associated with SCD scores (Radj2 = 46.69%; p = 6.37 × 10-12). Ultimately, we found that medial temporal lobe and frontal transcallosal tract microstructure is an important driver of SCD scores independent of early amyloid deposition. Our results highlight the potential importance of abnormal white matter diffusivity as an early contributor to cognitive decline. These results also highlight the value of incorporating multiple biomarkers to help disentangle the mechanistic heterogeneity of SCD as an early stage of cognitive decline.

Free-water metrics in medial temporal lobe white matter tract projections relate to longitudinal cognitive decline.

Although hippocampal volume has served as a long-standing predictor of cognitive decline, diffusion magnetic resonance imaging studies of white matter have shown similar relationships. Still, it remains unclear if gray matter and white matter interact to predict cognitive impairment and longitudinal decline. Here, we investigate whether free-water (FW) and FW-corrected fractional anisotropy (FAT) within medial temporal lobe white matter tracts provides meaningful contribution to cognition and cognitive decline beyond hippocampal volume. Using data from the Vanderbilt Memory & Aging Project (n = 319), we found that FW was associated with baseline memory and executive function beyond that of hippocampal volume and other comorbidities. Longitudinal analyses demonstrated significant interactions of hippocampal volume and inferior longitudinal fasciculus (p = 0.043) and cingulum bundle (p = 0.025) FAT on memory decline and with fornix FAT (p = 0.025) on decline in executive function. Results suggest that FW metrics of white matter have a unique role in cognitive decline and should be included in theoretical models of aging, cerebrovascular disease, and Alzheimer's disease.

Magnetic Resonance Imaging and Neurofilament Light in the Differentiation of Parkinsonism.

OBJECTIVE: Accurate diagnosis is particularly challenging in Parkinson's disease (PD), multiple system atrophy (MSAp), and progressive supranuclear palsy (PSP). We compare the utility of 3 promising biomarkers to differentiate disease state and explain disease severity in parkinsonism: the Automated Imaging Differentiation in Parkinsonism (AID-P), the Magnetic Resonance Parkinsonism Index (MRPI), and plasma-based neurofilament light chain protein (NfL). METHODS: For each biomarker, the area under the curve (AUC) of receiver operating characteristic curves were quantified for PD versus MSAp/PSP and MSAp versus PSP and statistically compared. Unique combinations of variables were also assessed. Furthermore, each measures association with disease severity was determined using stepwise multiple regression. RESULTS: For PD versus MSAp/PSP, AID-P (AUC, 0.900) measures had higher AUC compared with NfL (AUC, 0.747) and MRPI (AUC, 0.669), P < 0.05. For MSAp versus PSP, AID-P (AUC, 0.889), and MRPI (AUC, 0.824) measures were greater than NfL (AUC, 0.537), P < 0.05. We then combined measures to determine if any unique combination provided enhanced accuracy and found that no combination performed better than the AID-P alone in differentiating parkinsonisms. Furthermore, we found that the AID-P demonstrated the highest association with the MDS-UPDRS (Radj2 -AID-P, 26.58%; NfL,15.12%; MRPI, 12.90%). CONCLUSIONS: Compared with MRPI and NfL, AID-P provides the best overall differentiation of PD versus MSAp/PSP. Both AID-P and MRPI are effective in differentiating MSAp versus PSP. Furthermore, combining biomarkers did not improve classification of disease state compared with using AID-P alone. The findings demonstrate in the current sample that the AID-P and MRPI are robust biomarkers for PD, MSAp, and PSP. © 2020 International Parkinson and Movement Disorder Society.

Neurite orientation dispersion and density imaging (NODDI) and free-water imaging in Parkinsonism.

Neurite orientation dispersion and density imaging (NODDI) uses a three-compartment model to probe brain tissue microstructure, whereas free-water (FW) imaging models two-compartments. It is unknown if NODDI detects more disease-specific effects related to neurodegeneration in Parkinson's disease (PD) and atypical Parkinsonism. We acquired multi- and single-shell diffusion imaging at 3 Tesla across two sites. NODDI (using multi-shell; isotropic volume [Viso]; intracellular volume [Vic]; orientation dispersion [ODI]) and FW imaging (using single-shell; FW; free-water corrected fractional anisotropy [FAt]) were compared with 44 PD, 21 multiple system atrophy Parkinsonian variant (MSAp), 26 progressive supranuclear palsy (PSP), and 24 healthy control subjects in the basal ganglia, midbrain/thalamus, cerebellum, and corpus callosum. There was elevated Viso in posterior substantia nigra across Parkinsonisms, and Viso, Vic, and ODI were altered in MSAp and PSP in the striatum, globus pallidus, midbrain, thalamus, cerebellum, and corpus callosum relative to controls. The mean effect size across regions for Viso was 0.163, ODI 0.131, Vic 0.122, FW 0.359, and FAt 0.125, with extracellular compartments having the greatest effect size. A key question addressed was if these techniques discriminate PD and atypical Parkinsonism. Both NODDI (AUC: 0.945) and FW imaging (AUC: 0.969) had high accuracy, with no significant difference between models. This study provides new evidence that NODDI and FW imaging offer similar discriminability between PD and atypical Parkinsonism, and FW had higher effect sizes for detecting Parkinsonism within regions across the basal ganglia and cerebellum.