Introduction: Relationships between brain atrophy patterns of typical aging and Alzheimer's disease (AD), white matter disease, cognition, and AD neuropathology were investigated via machine learning in a large harmonized magnetic resonance imaging database (11 studies; 10,216 subjects). Methods: Three brain signatures were calculated: Brain-age, AD-like neurodegeneration, and white matter hyperintensities (WMHs). Brain Charts measured and displayed the relationships of these signatures to cognition and molecular biomarkers of AD. Results: WMHs were associated with advanced brain aging, AD-like atrophy, poorer cognition, and AD neuropathology in mild cognitive impairment (MCI)/AD and cognitively normal (CN) subjects. High WMH volume was associated with brain aging and cognitive decline occurring in an ≈10-year period in CN subjects. WMHs were associated with doubling the likelihood of amyloid beta (Aβ) positivity after age 65. Brain aging, AD-like atrophy, and WMHs were better predictors of cognition than chronological age in MCI/AD. Discussion: A Brain Chart quantifying brain-aging trajectories was established, enabling the systematic evaluation of individuals' brain-aging patterns relative to this large consortium.

ultiple neurologic disorders are thought to arise from dysfunctional neuronal circuits. Modulation of malfunctioning circuits can be achieved with therapies such as deep brain stimulation (DBS) (1). In DBS, electrical stimulation is delivered through implanted brain electrodes (2,3). DBS is best established as a therapeutic tool for movement disorders such as Parkinson disease, essential tremor, and dystonia (1,3). DBS is also being investigated as a treatment for psychiatric (4) and cognitive disorders (3). To date, more than 150 000 individuals have been implanted with DBS worldwide (5). Due to safety concerns, the ability to undergo MRI following DBS implantation is highly restricted. Because patients receiving DBS may require a wide range of MRI sequences for clinical purposes, and because MRI has been shown to be a valuable research tool in this population, additional data expounding the safety profile of MRI in individuals receiving DBS would be beneficial. Owing to safety concerns, MRI guidelines for scanning individuals receiving DBS are restrictive, largely limiting diagnostic uses. Strict safety guidelines (6-8) have been implemented after MRI-related adverse events (9): two cases of implantable pulse generator (IPG) failure during 1.5-T brain MRI; one case of temporary peri-electrode edema

Background The majority of Parkinson's disease patients with deep brain stimulation (DBS) use a monopolar configuration, which presents challenges for EEG and MRI studies. The literature reports algorithms to convert monopolar to bipolar settings. Purpose/Hypothesis To assess brain responses of Parkinson's disease patients implanted with DBS during fMRI studies using their clinical and presumed equivalent settings using a published conversion recipe. Study Type Prospective. Subjects Thirteen DBS patients. Field Strength/Sequence 1.5T and 3T, fMRI using gradient echo‐planar imaging. Assessment Patients underwent 30/30sec ON/OFF DBS fMRI scans using monopolar and bipolar settings. To convert to a bipolar setting, the negative contact used for the monopolar configuration remained constant and the adjacent dorsal contact was rendered positive, while increasing the voltage by 30%. fMRI activation/deactivation maps and motor Unified Parkinson's Disease Rating Scale (UPDRS‐III) scores were compared for patients in both configurations. Statistical Tests T‐tests were used to compare UPDRS scores and volumes of tissue activated (VTA) diameters in monopolar and bipolar configurations. Results The patterns of fMRI activation in the monopolar and bipolar configurations were generally different. The thalamus, pallidum, and visual cortices exhibited higher activation using the patient's clinical settings than the presumed equivalent settings. VTA diameters were lower (7 mm vs. 6.3 mm, P = 0.047) and UPDRS scores were generally higher in the bipolar (33.2 ± 16) than in the monopolar configuration (28.3 ± 17.4), without reaching statistical significance (P > 0.05). Data Conclusion Monopolar and bipolar configurations result in different patterns of brain activation while using a previously published monopolar–bipolar conversion algorithm. Clinical benefits may be achieved with varying patterns of brain responses. Blind conversion from one to the other should be avoided for purposes of understanding the mechanisms of DBS. Level of Evidence: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018.

Neuroimaging biomarkers that distinguish between changes due to typical brain aging and Alzheimer’s disease are valuable for determining how much each contributes to cognitive decline. Supervised machine learning models can derive multi-variate patterns of brain change related to the two processes, including the SPARE-AD (Spatial Patterns of Atrophy for Recognition of Alzheimer’s Disease) and SPARE-BA (of Brain Aging) scores investigated herein. However, the substantial overlap between brain regions affected in the two processes confounds measuring them independently. We present a methodology, and associated results, toward disentangling the two. T1-weighted MRI scans of 4,054 participants (48–95 years) with Alzheimer’s disease, mild cognitive impairment, or cognitively normal diagnoses from the iSTAGING (Imaging-based coordinate SysTem for AGIng and NeurodeGenerative diseases) consortium were analyzed. Multiple sets of SPARE scores were investigated, in order to probe imaging signatures of certain clinically- or molecularly-defined sub-cohorts. First, a subset of clinical Alzheimer’s disease patients (n = 718) and age- and sex-matched cognitively normal adults (n = 718) were selected based purely on clinical diagnoses to train SPARE-BA1 (regression of age using cognitively normal individuals) and SPARE-AD1 (classification of cognitively normal versus Alzheimer’s disease) models. Second, analogous groups were selected based on clinical and molecular markers to train SPARE-BA2 and SPARE-AD2 models: amyloid-positive Alzheimer’s disease continuum group (n = 718; consisting of amyloid-positive Alzheimer’s disease, amyloid-positive mild cognitive impairment, and amyloid- and tau-positive cognitively normal individuals) and amyloid-negative cognitively normal group (n = 718). Finally, the combined group of the Alzheimer’s disease continuum and amyloid-negative cognitively normal individuals was used to train SPARE-BA3 model, with the intention to estimate brain age regardless of Alzheimer’s disease-related brain changes. The disentangled SPARE models, SPARE-AD2 and SPARE-BA3, derived brain patterns that were more specific to the two types of the brain changes. The correlation between the SPARE-BA Gap (SPARE-BA minus chronological age) and SPARE-AD was significantly reduced after the decoupling (r = 0.56 to 0.06). The correlation of disentangled SPARE-AD was non-inferior to amyloid- and tau-related measurements and to the number of APOE ε4 alleles but was lower to Alzheimer’s disease-related psychometric test scores, suggesting contribution of advanced brain aging to the latter. The disentangled SPARE-BA was consistently less correlated with Alzheimer’s disease-related clinical, molecular, and genetic variables. By employing conservative molecular diagnoses and introducing Alzheimer’s disease continuum cases to the SPARE-BA model training, we achieved more dissociable neuroanatomical biomarkers of typical brain aging and Alzheimer’s disease.

Lobar cerebral microbleeds (CMBs) and localized non-hemorrhage iron deposits in the basal ganglia have been associated with brain aging, vascular disease and neurodegenerative disorders. Particularly, CMBs are small lesions and require multiple neuroimaging modalities for accurate detection. Quantitative susceptibility mapping (QSM) derived from in vivo magnetic resonance imaging (MRI) is necessary to differentiate between iron content and mineralization. We set out to develop a deep learning-based segmentation method suitable for segmenting both CMBs and iron deposits. We included a convenience sample of 24 participants from the MESA cohort and used T2-weighted images, susceptibility weighted imaging (SWI), and QSM to segment the two types of lesions. We developed a protocol for simultaneous manual annotation of CMBs and non-hemorrhage iron deposits in the basal ganglia. This manual annotation was then used to train a deep convolution neural network (CNN). Specifically, we adapted the U-Net model with a higher number of resolution layers to be able to detect small lesions such as CMBs from standard resolution MRI. We tested different combinations of the three modalities to determine the most informative data sources for the detection tasks. In the detection of CMBs using single class and multiclass models, we achieved an average sensitivity and precision of between 0.84–0.88 and 0.40–0.59, respectively. The same framework detected non-hemorrhage iron deposits with an average sensitivity and precision of about 0.75–0.81 and 0.62–0.75, respectively. Our results showed that deep learning could automate the detection of small vessel disease lesions and including multimodal MR data (particularly QSM) can improve the detection of CMB and non-hemorrhage iron deposits with sensitivity and precision that is compatible with use in large-scale research studies.