A recent shift in legal and social attitudes toward marijuana use has also spawned a surge of interest in understanding the effects of marijuana use on the brain. There is considerable evidence that an adolescent onset of marijuana use negatively impacts white matter coherence. On the other hand, a recent well-controlled study demonstrated no effects of marijuana use on the morphometry of subcortical or cortical structures when users and non-users were matched for alcohol use. Regardless, most studies have involved small, carefully selected samples, so the ability to generalize to larger populations is limited. In an attempt to address this issue, we examined the effects of marijuana use on white matter integrity and cortical and subcortical morphometry using data from the Human Connectome Project (HCP) consortium. The HCP data consists of ultra-high resolution neuroimaging data from a large community sample, including 466 adults reporting recreational marijuana use. Rather than just contrasting two groups of individuals who vary significantly in marijuana usage as typifies prior studies, we leveraged the large sample size provided by the HCP data to examine parametric effects of recreational marijuana use. Our results indicate that the earlier the age of onset of marijuana use, the lower was white matter coherence. Age of onset also also affected the shape of the accumbens, while the number of lifetime uses impacted the shape of the amygdala and hippocampus. Marijuana use had no effect on cortical volumes. These findings suggest subtle but significant effects of recreational marijuana use on brain structure.

The formation of toxic Amyloid β-peptide (Aβ) oligomers is one of the earliest events in the molecular pathology of Alzheimer’s Disease (AD). These oligomers lead to a variety of downstream effects, including impaired neuronal signaling, neuroinflammation, tau phosphorylation, and neurodegeneration, and it is estimated that these events begin 10 to 20 y before the presentation of symptoms. Toxic Aβ oligomers contain a nonstandard protein structure, termed α-sheet, and designed α–sheet peptides target this main-chain structure in toxic oligomers independent of sequence. Here we show that a designed α–sheet peptide inhibits the deleterious effects on neuronal signaling and also serves as a capture agent in our soluble oligomer binding assay (SOBA). Pre-incubated synthetic α–sheet-containing Aβ oligomers produce strong SOBA signals, while monomeric and β-sheet protofibrillar Aβ do not. α–sheet containing oligomers were also present in cerebrospinal fluid (CSF) from an AD patient versus a noncognitively impaired control. For the detection of toxic oligomers in plasma, we developed a plate coating to increase the density of the capture peptide. The proof of concept was achieved by testing 379 banked human plasma samples. SOBA detected Aβ oligomers in patients on the AD continuum, including controls who later progressed to mild cognitive impairment. In addition, SOBA discriminated AD from other forms of dementia, yielding sensitivity and specificity of 99% relative to clinical and neuropathological diagnoses. To explore the broader potential of SOBA, we adapted the assay for a-synuclein oligomers and confirmed their presence in CSF from patients with Parkinson’s disease and Lewy body dementia.

Understanding how exercise affects communication across the brain in overweight/obese individuals may provide insight into mechanisms of weight loss and maintenance. In the current study, we examined the effects of a 6-month exercise program in 11 overweight/obese individuals (mean BMI: 33.6±1.4 mg/kg2; mean age: 38.2±3.2 years) on integrative brain “hubs,” which are areas with high levels of connectivity to multiple large-scale networks thought to play an important role in multimodal integration among brain regions. These integrative hubs were identified with a recently developed between-network connectivity (BNC) metric, using functional magnetic resonance imaging (fMRI). BNC utilizes a multiple regression analysis approach to assess relationships between the time series of large-scale functionally-connected brain networks (identified using independent components analysis) and the time series of each individual voxel in the brain. This approach identifies brain regions with high between-network interaction, i.e., areas with high levels of connectivity to many large-scale networks. Changes in BNC following exercise were determined using paired t-tests, with results considered significant at a whole-brain level if they exceeded a voxel-wise threshold of p < 0.01 and cluster-level family-wise error (FWE) correction for multiple comparisons of p < 0.05. Following the intervention, BNC in the posterior cingulate cortex (PCC) was significantly reduced (p < 0.001). The changes driving the observed effects were explored using Granger causality, finding significant reductions in both outgoing causal flow from the PCC to a number of networks (p < 0.05; language network, visual network, sensorimotor network, left executive control network, basal ganglia network, posterior default mode network), in addition to reductions in ingoing causal flow to the PCC from a number of networks (p < 0.05; ventral default mode network, language network, sensorimotor network, basal ganglia network). Change in BNC was related to changes in aerobic fitness level (VO2 max; p = 0.008) and perceived hunger (Three Factor Eating Questionnaire; p = 0.040). Overall, the impact of exercise on communication between large-scale networks may contribute to individual responsivity to exercise.

No abstract