Diffusion tensor imaging has traditionally been used to assess white matter (WM) integrity in Alzheimer’s disease (AD). However, the tensor model is limited in modeling complex WM structure. Neurite Orientation Dispersion and Density Imaging (NODDI), a cutting-edge technique applied to multishell diffusion MRI, can offer more precise insights into microstructural features of WM integrity. We assessed whether NODDI more sensitively detects AD-related changes in medial temporal lobe (MTL) WM than traditional tensor metrics. In total, 199 older adults with multishell diffusion MRI from ADNI3 (mean age = 75 years; 60% female; cognitively unimpaired, n = 121; cognitively impaired MCI/dementia, n = 77) were analyzed. Tensor metrics of fractional anisotropy (FA) and mean diffusivity (MD), as well as NODDI metrics of Neurite Density Index (NDI) and orientation dispersion Index (ODI), were calculated for MTL WM tracts (JHU Atlas: hippocampal cingulum, fornix column/body, fornix/stria terminalis, and uncinate). A subset of participants received 18F-florbetapir or 18F-florbetaben to measure Aβ (n = 146; converted to Centiloids), 18F-flortaucipir to measure tau (n = 135), and neuropsychological testing including the Clinical Dementia Rating Sum of Boxes (CDR-SB) and memory composite score (ADNI-MEM). NODDI measures in MTL tracts were more strongly correlated with cognitive performance and AD pathology than standard tensor measures. For example, entorhinal tau was strongly associated with NDI in the cingulum hippocampus and the uncinate, and with ODI in the fornix ST. Both ODI and NDI across the majority of tracts were associated with CDR-SB and ADNI-MEM. In contrast, FA in any MTL tract was not significantly correlated with either tau or global amyloid-beta, while MD in MTL tracts showed limited correlations with pathology or cognition. NDI partially mediated the relationship between AD pathology (entorhinal tau, meta temporal tau, or Aβ) and the memory composite score. Random forest modeling showed that a combination of NODDI metrics and DTI had the strongest estimates of memory performance and clinical impairment. NODDI metrics offer more sensitive insights about MTL WM integrity in AD that could have been previously missed due to the limitations of DTI analyses. Additionally, combining NODDI with DTI yielded the strongest predictive performance for memory and clinical impairment, suggesting that the two approaches are complementary. The use of advanced diffusion acquisitions such as multishell which allows for analyses such as NODDI is crucial for the future development of disease identification and structural understanding.

Despite extensive research, open questions about the biological underpinnings of Alzheimer’s disease (AD) remain. Neuroimaging biomarkers based on positron emission tomography (PET) and magnetic resonance imaging (MRI) offer in vivo insights into these complex biological changes and interactions. However, most evidence to date comes from cross-sectional studies, limiting our understanding of disease progression. Longitudinal studies enable the investigation of biological changes within individuals, revealing how pathology evolves over time. With this review, we provide an overview of how longitudinal imaging biomarker studies have advanced the field and how they can contribute to future research. We highlight longitudinal biomarker studies that have provided critical insights into disease trajectories, staging, and individual variability. We further assess longitudinal multimodal studies which have elucidated interactions between AD-specific pathology, amyloid-β and tau, and broader biological changes like neurodegeneration, neuronal dysfunction, vascular disease, and inflammation. Further, we discuss associations of brain changes with symptomatology and clinical outcomes and conclude with challenges and future directions.

Disease-modifying treatments have transformed the natural history of spinal muscular atrophy (SMA), but the cellular pathways altered by SMN restoration remain undefined and biomarkers cannot yet precisely predict treatment response. We performed an exploratory cerebrospinal fluid (CSF) proteomic study in a diverse sample of SMA patients treated with nusinersen to elucidate therapeutic pathways and identify predictors of motor improvement. Proteomic analyses were performed on CSF samples collected before treatment (T0) and at 6 months (T6) using an Olink panel to quantify 1113 peptides. A supervised machine learning approach was used to identify proteins that discriminated patients who improved functionally from those who did not after 2 years of treatment. A total of 49 SMA patients were included (10 type 1, 18 type 2, and 21 type 3), ranging in age from 3 months to 65 years. Most proteins showed a decrease in CSF concentration at T6. The machine learning algorithm identified ARSB, ENTPD2, NEFL, and IFI30 as the proteins most predictive of improvement. The machine learning model was able to predict motor improvement at 2 years with 79.6% accuracy. The results highlight the potential application of CSF biomarkers to predict motor improvement following SMA treatment. Validation in larger datasets is needed.

INTRODUCTION: Diffusion tensor imaging has been used to assess white matter (WM) changes in the early stages of Alzheimer’s disease (AD). However, the tensor model is necessarily limited by its assumptions. Neurite Orientation Dispersion and Density Imaging (NODDI) can offer insights into microstructural features of WM change. We assessed whether NODDI more sensitively detects AD-related changes in medial temporal lobe WM than traditional tensor metrics. METHODS: Standard diffusion and NODDI metrics were calculated for medial temporal WM tracts from 199 older adults drawn from ADNI3 who also received PET to measure pathology and neuropsychological testing. RESULTS: NODDI measures in medial temporal tracts were more strongly correlated to cognitive performance and pathology than standard measures. The combination of NODDI and standard metrics exhibited the strongest prediction of cognitive performance in random forest analyses. CONCLUSIONS: NODDI metrics offer additional insights into contributions of WM degeneration to cognitive outcomes in the aging brain.

Assessing endurance in non-ambulatory individuals with Spinal Muscular Atrophy (SMA) has been challenging due to limited evaluation tools. The Assisted 6-Minute Cycling Test (A6MCT) is an upper limb ergometer assessment used in other neurologic disorders to measure endurance. To study the performance of the A6MCT in the non-ambulatory SMA population, prospective data was collected on 38 individuals with SMA (13 sitters; 25 non-sitters), aged 5 to 74 years (mean = 30.3; SD = 14.1). The clinical measures used were A6MCT, Revised Upper Limb Module (RULM), Adapted Test of Neuromuscular Disorders (ATEND), and Egen Klassifikation Scale 2 (EK2). Perceived fatigue was assessed using the Fatigue Severity Scale (FSS), and effort was assessed using the Rate of Perceived Exertion (RPE). Data were analyzed for: (1) Feasibility, (2) Clinical discrimination, and (3) Associations between A6MCT with clinical characteristics and outcomes. Results showed the A6MCT was feasible for 95% of the tested subjects, discriminated between functional groups (p = 0.0086), and was significantly associated with results obtained from RULM, ATEND, EK2, and Brooke (p < 0.0001; p = 0.029; p < 0.001; p = 0.005). These findings indicate the A6MCT’s potential to evaluate muscular endurance in non-ambulatory SMA individuals, complementing clinician-rated assessments. Nevertheless, further validation with a larger dataset is needed for broader application.

Background: Damage to white matter (WM) within the medial temporal lobe (MTL) may underlie memory decline in aging and Alzheimer’s disease (AD). We measured MTL WM tract integrity through diffusion tensor imaging and Neurite Orientation Dispersion and Density Imaging (NODDI). We tested WM integrity associations with memory performance and amyloid‐ß pathology in cognitively normal older adults.Method: 105 cognitively normal older adults (70.8 years old, 65% female) with diffusion MRI were processed with MRtrix3. Standard diffusion metrics of fractional anisotropy (FA) and mean diffusivity (MD) were calculated. NODDI MDT was used to generate the neurite density index (NDI), representing number of voxels with unhindered diffusion, and the orientation dispersion index (ODI), representing the variability of neurite orientation. We calculated these measures for MTL tracts from the JHU WM Atlas: cingulum‐hippocampus, fornix column/body, and fornix/stria terminalis. Participants received 18F‐florbetapir (FBP) to measure Aß pathology (n = 94), and global FBP SUVR was calculated. Memory was measured with the Rey Auditory Verbal Learning Test delayed recall score (RAVLT, n = 81). Analyses covaried for age and/or education.Result: Better memory was associated with lower ODI in the fornix column/body (r = ‐0.27, p = 0.018), with trends for higher FA (r = 0.021, p = 0.06) and lower NDI (r = ‐0.19, p = 0.09). Better memory was also associated with higher MD in the cingulum‐hippocampus (right: r = 0.23, p = 0.04; left: r = 0.25, p = 0.03), with trends for lower ODI (right: r = ‐.20, p = 0.08; left: r = ‐0.20, p = 0.09). Higher FBP SUVR was associated with increased FA in the fornix column/body (r = 0.31, p = 0.003) and a trend in the left cingulum hippocampus (r = 0.20, p = 0.054). Memory or FBP SUVR was not significantly related to integrity of the fornix/stria terminalis.Conclusion: Integrity of fornix column/body and cingulum‐hippocampus were related to both memory performance and Aß pathology. Our results suggest that using a combination of NODDI as well as diffusion tensor metrics provided a more robust understanding of memory impacts in aging.