Alterations in brain structure related to breast cancer and its treatment: Chemotherapy and other considerations

Cognitive effects of cancer and its treatment have been a topic of increasing investigation over the past ∼30 years. Recent studies have focused on better understanding the neural correlates of these effects, with an emphasis on post-chemotherapy effects in breast cancer patients. Structural MRI studies have utilized both automated and manual approaches to quantify gray and white matter characteristics (e.g., regional volume and density) in breast cancer patients treated with chemotherapy relative to patients who did not receive chemotherapy and/or healthy controls. While most work to date has been retrospective, a small number of baseline (pre-systemic therapy) and prospective longitudinal studies have been conducted. Data have consistently shown lower gray and white matter volume and density in patients treated with chemotherapy, particularly in frontal and temporal brain regions. Host factors and/or the cancer disease process and other therapies (e.g., antiestrogen treatment) also seem likely to contribute to the observed differences, though the relative contributions of these effects have not yet been investigated in detail. These structural abnormalities have been shown to relate to subjective and objective cognitive functioning, as well as to biological factors that may help to elucidate the underlying mechanism(s). This review examines the currently available published observations and discusses the major themes and promising directions for future studies

Neuroimaging biomarkers of neurodegenerative diseases and dementia

Neurodegenerative disorders leading to dementia are common diseases that affect many older and some young adults. Neuroimaging methods are important tools for assessing and monitoring pathological brain changes associated with progressive neurodegenerative conditions. In this review, the authors describe key findings from neuroimaging studies (magnetic resonance imaging and radionucleotide imaging) in neurodegenerative disorders, including Alzheimer's disease (AD) and prodromal stages, familial and atypical AD syndromes, frontotemporal dementia, amyotrophic lateral sclerosis with and without dementia, Parkinson's disease with and without dementia, dementia with Lewy bodies, Huntington's disease, multiple sclerosis, HIV-associated neurocognitive disorder, and prion protein associated diseases (i.e., Creutzfeldt-Jakob disease). The authors focus on neuroimaging findings of in vivo pathology in these disorders, as well as the potential for neuroimaging to provide useful information for differential diagnosis of neurodegenerative disorders

Brain Rehabilitation, Advanced Imaging, and Neuroscience (BRAIN): An IUPUI Signature Center Initiative (SCI)

poster abstractAbstract The Mission of the Indiana Center for Brain Rehabilitation, Advanced Imaging, and Neuroscience (ICBRAIN) is: to develop and disseminate techniques and methodologies for combining advanced neuroimaging, neurogenetics and other neurophysiological measures with precision behavioral measurement to evaluate novel rehabilitation interventions for people with acquired brain injury. Traumatic and other types of acquired brain injury (ABI) affect millions of U.S. citizens each year, many of whom experience persistent disabilities. Over the past decade there has been a notable rise in research activities to address serious gaps in the knowledge base of ABI, including neuroimaging, outcome measurement, and intervention studies to change function. However, brain injury researchers have not yet established solid links between these research agendas. The BRAIN SCI fills this gap in neuroscience by bringing together an interdisciplinary team of clinical researchers to (1) advance basic science and clinical knowledge to the next level of integration, (2) translate the knowledge gained into clinical care for improved patient outcomes, and (3) use the newly integrated knowledge to drive the leading edge of translational research. BRAIN research includes the Indiana Traumatic Brain Injury Model System, funded by the National Institute for Disability and Rehabilitation Research (NIDRR), the InterFACE Center for the study of emotions and interpersonal interactions after neurologic injury, and 12 externally funded research projects. BRAIN research ranges from development of a neurogenetic respository and advanced neuroimaging studies to determine critical elements in recovery from brain injury to intervention studies to improve recovery to a multi-national study of an intervention for phantom limb pain. BRAIN research is transdisciplinary. Disciplines currently involved in BRAIN research include physiatry, neuropsychology, neuroradiology, rehabilitation science, biomedical engineering, and psychiatry. The Indiana University School of Medicine Neuroscience Center provides a home for BRAIN and supports its interdisciplinary Steering Committee. In addition to partnerships with the Neuroscience Center, the Center for Neuroimaging, and the InterFACE Center, BRAIN collaborates with the Rehabilitation Hospital of Indiana, the Stark Neuroscience Institute, and the School of Health and Rehabilitation Sciences. This presentation will describe BRAIN’s mission, vision, values, strategic plan, organization, partnerships, and ongoing research projects in greater detail

Deep Learning in Alzheimer's Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data

Deep learning, a state-of-the-art machine learning approach, has shown outstanding performance over traditional machine learning in identifying intricate structures in complex high-dimensional data, especially in the domain of computer vision. The application of deep learning to early detection and automated classification of Alzheimer's disease (AD) has recently gained considerable attention, as rapid progress in neuroimaging techniques has generated large-scale multimodal neuroimaging data. A systematic review of publications using deep learning approaches and neuroimaging data for diagnostic classification of AD was performed. A PubMed and Google Scholar search was used to identify deep learning papers on AD published between January 2013 and July 2018. These papers were reviewed, evaluated, and classified by algorithm and neuroimaging type, and the findings were summarized. Of 16 studies meeting full inclusion criteria, 4 used a combination of deep learning and traditional machine learning approaches, and 12 used only deep learning approaches. The combination of traditional machine learning for classification and stacked auto-encoder (SAE) for feature selection produced accuracies of up to 98.8% for AD classification and 83.7% for prediction of conversion from mild cognitive impairment (MCI), a prodromal stage of AD, to AD. Deep learning approaches, such as convolutional neural network (CNN) or recurrent neural network (RNN), that use neuroimaging data without pre-processing for feature selection have yielded accuracies of up to 96.0% for AD classification and 84.2% for MCI conversion prediction. The best classification performance was obtained when multimodal neuroimaging and fluid biomarkers were combined. Deep learning approaches continue to improve in performance and appear to hold promise for diagnostic classification of AD using multimodal neuroimaging data. AD research that uses deep learning is still evolving, improving performance by incorporating additional hybrid data types, such as-omics data, increasing transparency with explainable approaches that add knowledge of specific disease-related features and mechanisms

Hippocampal Sclerosis of Aging, a Common Alzheimer's Disease 'Mimic': Risk Genotypes are Associated with Brain Atrophy Outside the Temporal Lobe

Hippocampal sclerosis of aging (HS-Aging) is a common brain disease in older adults with a clinical course that is similar to Alzheimer's disease. Four single-nucleotide polymorphisms (SNPs) have previously shown association with HS-Aging. The present study investigated structural brain changes associated with these SNPs using surface-based analysis. Participants from the Alzheimer's Disease Neuroimaging Initiative cohort (ADNI; n = 1,239), with both MRI scans and genotype data, were used to assess the association between brain atrophy and previously identified HS-Aging risk SNPs in the following genes: GRN, TMEM106B, ABCC9, and KCNMB2 (minor allele frequency for each is >30%). A fifth SNP (near the ABCC9 gene) was evaluated in post-hoc analysis. The GRN risk SNP (rs5848_T) was associated with a pattern of atrophy in the dorsomedial frontal lobes bilaterally, remarkable since GRN is a risk factor for frontotemporal dementia. The ABCC9 risk SNP (rs704180_A) was associated with multifocal atrophy whereas a SNP (rs7488080_A) nearby (∼50 kb upstream) ABCC9 was associated with atrophy in the right entorhinal cortex. Neither TMEM106B (rs1990622_T), KCNMB2 (rs9637454_A), nor any of the non-risk alleles were associated with brain atrophy. When all four previously identified HS-Aging risk SNPs were summed into a polygenic risk score, there was a pattern of associated multifocal brain atrophy in a predominately frontal pattern. We conclude that common SNPs previously linked to HS-Aging pathology were associated with a distinct pattern of anterior cortical atrophy. Genetic variation associated with HS-Aging pathology may represent a non-Alzheimer's disease contribution to atrophy outside of the hippocampus in older adults

Imaging Brain Networks After Cancer and Chemotherapy: Advances Toward Etiology and Unanswered Questions

Comment on Neurotoxic Effects of Anthracycline- vs Nonanthracycline-Based Chemotherapy on Cognition in Breast Cancer Survivors. [JAMA Oncol. 2016