Jeffrey N. Browndyke

Aerobic Exercise and Neurocognitive Performance: A Meta-analytic Review of Randomized Controlled Trials

Objectives: To assess the effects of aerobic exercise training on neurocognitive performance. Although the effects of exercise on neurocognition have been the subject of several previous reviews and meta-analyses, they have been hampered by methodological shortcomings and are now outdated as a result of the recent publication of several large-scale, randomized, controlled trials (RCTs). Methods: We conducted a systematic literature review of RCTs examining the association between aerobic exercise training on neurocognitive performance between January 1966 and July 2009. Suitable studies were selected for inclusion according to the following criteria: randomized treatment allocation; mean age ≥18 years of age; duration of treatment >1 month; incorporated aerobic exercise components; supervised exercise training; the presence of a nonaerobic-exercise control group; and sufficient information to derive effect size data. Results: Twenty-nine studies met inclusion criteria and were included in our analyses, representing data from 2049 participants and 234 effect sizes. Individuals randomly assigned to receive aerobic exercise training demonstrated modest improvements in attention and processing speed (g = 0.158; 95% confidence interval [CI]; 0.055–0.260; p = .003), executive function (g = 0.123; 95% CI, 0.021–0.225; p = .018), and memory (g = 0.128; 95% CI, 0.015–0.241; p = .026). Conclusions: Aerobic exercise training is associated with modest improvements in attention and processing speed, executive function, and memory, although the effects of exercise on working memory are less consistent. Rigorous RCTs are needed with larger samples, appropriate controls, and longer follow-up periods. ITT = intention-to-treat; RCT = randomized controlled trial.

Identification of MCI individuals using structural and functional connectivity networks.

Different imaging modalities provide essential complementary information that can be used to enhance our understanding of brain disorders. This study focuses on integrating multiple imaging modalities to identify individuals at risk for mild cognitive impairment (MCI). MCI, often an early stage of Alzheimers disease (AD), is difficult to diagnose due to its very mild or insignificant symptoms of cognitive impairment. Recent emergence of brain network analysis has made characterization of neurological disorders at a whole-brain connectivity level possible, thus providing new avenues for brain diseases classification. Employing multiple-kernel Support Vector Machines (SVMs), we attempt to integrate information from diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI) for improving classification performance. Our results indicate that the multimodality classification approach yields statistically significant improvement in accuracy over using each modality independently. The classification accuracy obtained by the proposed method is 96.3%, which is an increase of at least 7.4% from the single modality-based methods and the direct data fusion method. A cross-validation estimation of the generalization performance gives an area of 0.953 under the receiver operating characteristic (ROC) curve, indicating excellent diagnostic power. The multimodality classification approach hence allows more accurate early detection of brain abnormalities with greater sensitivity.

Effects of the Dietary Approaches to Stop Hypertension Diet, Exercise, and Caloric Restriction on Neurocognition in Overweight Adults With High Blood Pressure

High blood pressure increases the risks of stroke, dementia, and neurocognitive dysfunction. Although aerobic exercise and dietary modifications have been shown to reduce blood pressure, no randomized trials have examined the effects of aerobic exercise combined with dietary modification on neurocognitive functioning in individuals with high blood pressure (ie, prehypertension and stage 1 hypertension). As part of a larger investigation, 124 participants with elevated blood pressure (systolic blood pressure 130 to 159 mm Hg or diastolic blood pressure 85 to 99 mm Hg) who were sedentary and overweight or obese (body mass index: 25 to 40 kg/m2) were randomized to the Dietary Approaches to Stop Hypertension (DASH) diet alone, DASH combined with a behavioral weight management program including exercise and caloric restriction, or a usual diet control group. Participants completed a battery of neurocognitive tests of executive function-memory-learning and psychomotor speed at baseline and again after the 4-month intervention. Participants on the DASH diet combined with a behavioral weight management program exhibited greater improvements in executive function-memory-learning (Cohens D=0.562; P=0.008) and psychomotor speed (Cohens D=0.480; P=0.023), and DASH diet alone participants exhibited better psychomotor speed (Cohens D=0.440; P=0.036) compared with the usual diet control. Neurocognitive improvements appeared to be mediated by increased aerobic fitness and weight loss. Also, participants with greater intima-medial thickness and higher systolic blood pressure showed greater improvements in executive function-memory-learning in the group on the DASH diet combined with a behavioral weight management program. In conclusion, combining aerobic exercise with the DASH diet and caloric restriction improves neurocognitive function among sedentary and overweight/obese individuals with prehypertension and hypertension.

Enriched white matter connectivity networks for accurate identification of MCI patients.

Mild cognitive impairment (MCI), often a prodromal phase of Alzheimers disease (AD), is frequently considered to be a good target for early diagnosis and therapeutic interventions of AD. Recent emergence of reliable network characterization techniques has made it possible to understand neurological disorders at a whole-brain connectivity level. Accordingly, we propose an effective network-based multivariate classification algorithm, using a collection of measures derived from white matter (WM) connectivity networks, to accurately identify MCI patients from normal controls. An enriched description of WM connections, utilizing six physiological parameters, i.e., fiber count, fractional anisotropy (FA), mean diffusivity (MD), and principal diffusivities(λ(1), λ(2), and λ(3)), results in six connectivity networks for each subject to account for the connection topology and the biophysical properties of the connections. Upon parcellating the brain into 90 regions-of-interest (ROIs), these properties can be quantified for each pair of regions with common traversing fibers. For building an MCI classifier, clustering coefficient of each ROI in relation to the remaining ROIs is extracted as feature for classification. These features are then ranked according to their Pearson correlation with respect to the clinical labels, and are further sieved to select the most discriminant subset of features using an SVM-based feature selection algorithm. Finally, support vector machines (SVMs) are trained using the selected subset of features. Classification accuracy was evaluated via leave-one-out cross-validation to ensure generalization of performance. The classification accuracy given by our enriched description of WM connections is 88.9%, which is an increase of at least 14.8% from that using simple WM connectivity description with any single physiological parameter. A cross-validation estimation of the generalization performance shows an area of 0.929 under the receiver operating characteristic (ROC) curve, indicating excellent diagnostic power. It was also found, based on the selected features, that portions of the prefrontal cortex, orbitofrontal cortex, parietal lobe and insula regions provided the most discriminant features for classification, in line with results reported in previous studies. Our MCI classification framework, especially the enriched description of WM connections, allows accurate early detection of brain abnormalities, which is of paramount importance for treatment management of potential AD patients.

Temporal lobe functional activity and connectivity in young adult APOE ɛ4 carriers

We sought to determine if the APOE ε4 allele influences both the functional activation and connectivity of the medial temporal lobes (MTLs) during successful memory encoding in young adults.

Applications of computer-based neuropsychological assessment

Objectives:To present current applications of computer-based neuropsychological assessment, including the assessment of sports-related concussion, symptom validity testing, and the remote administration of tests through the Internet. Problem areas:If computer-based assessment benefits are to become popularized, a few issues will need to be addressed: the development of psychometric data based on comparisons with long-standing empirically sound test measures; additional validation of measures by parties not involved in their commercial development; increased focus on ecological validity; exploration of the usefulness of remote data storage and automated posting to databases; and improved documentation of specific computer hardware and software used in experimental methods. Conclusions:Beyond ease of administration and data collection, computer-based assessment offers benefits over paper-and-pencil measures in the form of millisecond timing accuracy, reliable and randomized presentation of stimuli over multiple trials and repeat administrations, and unobtrusive measurement of cognitive skills and response times during all aspects of the assessment process.

Genetic Regulation of α-Synuclein mRNA Expression in Various Human Brain Tissues

Genetic variability across the SNCA locus has been repeatedly associated with susceptibility to sporadic Parkinsons disease (PD). Accumulated evidence emphasizes the importance of SNCA dosage and expression levels in PD pathogenesis. However whether genetic variability in the SNCA gene modulates the risk to develop sporadic PD via regulation of SNCA expression remained elusive. We studied the effect of PD risk-associated variants at SNCA 5′ and 3′regions on SNCA-mRNA levels in vivo in 228 human brain samples from three structures differentially vulnerable to PD pathology (substantia-nigra, temporal- and frontal-cortex) obtained from 144 neurologically normal cadavers. The extensively characterized PD-associated promoter polymorphism, Rep1, had an effect on SNCA-mRNA levels. Homozygous genotype of the ‘protective’, Rep1-259 bp allele, was associated with lower levels of SNCA-mRNA relative to individuals that carried at least one copy of the PD-risk associated alleles, amounting to an average decrease of ∼40% and >50% in temporal-cortex and substantia-nigra, respectively. Furthermore, SNPs tagging the SNCA 3′-untranslated-region also showed effects on SNCA-mRNA levels in both the temporal-cortex and the substantia-nigra, although, in contrast to Rep1, the ‘decreased-risk’ alleles were correlated with increased SNCA-mRNA levels. Similar to Rep1 findings, no difference in SNCA-mRNA level was seen with different SNCA 3′SNP alleles in the frontal-cortex, indicating there is brain-region specificity of the genetic regulation of SNCA expression. We provide evidence for functional consequences of PD-associated SNCA gene variants in disease relevant brain tissues, suggesting that genetic regulation of SNCA expression plays an important role in the development of the disease.

Resting-State Multi-Spectrum Functional Connectivity Networks for Identification of MCI Patients

In this paper, a high-dimensional pattern classification framework, based on functional associations between brain regions during resting-state, is proposed to accurately identify MCI individuals from subjects who experience normal aging. The proposed technique employs multi-spectrum networks to characterize the complex yet subtle blood oxygenation level dependent (BOLD) signal changes caused by pathological attacks. The utilization of multi-spectrum networks in identifying MCI individuals is motivated by the inherent frequency-specific properties of BOLD spectrum. It is believed that frequency specific information extracted from different spectra may delineate the complex yet subtle variations of BOLD signals more effectively. In the proposed technique, regional mean time series of each region-of-interest (ROI) is band-pass filtered ( Hz) before it is decomposed into five frequency sub-bands. Five connectivity networks are constructed, one from each frequency sub-band. Clustering coefficient of each ROI in relation to the other ROIs are extracted as features for classification. Classification accuracy was evaluated via leave-one-out cross-validation to ensure generalization of performance. The classification accuracy obtained by this approach is 86.5%, which is an increase of at least 18.9% from the conventional full-spectrum methods. A cross-validation estimation of the generalization performance shows an area of 0.863 under the receiver operating characteristic (ROC) curve, indicating good diagnostic power. It was also found that, based on the selected features, portions of the prefrontal cortex, orbitofrontal cortex, temporal lobe, and parietal lobe regions provided the most discriminant information for classification, in line with results reported in previous studies. Analysis on individual frequency sub-bands demonstrated that different sub-bands contribute differently to classification, providing extra evidence regarding frequency-specific distribution of BOLD signals. Our MCI classification framework, which allows accurate early detection of functional brain abnormalities, makes an important positive contribution to the treatment management of potential AD patients.

Perfusion abnormalities and decision making in cocaine dependence.

BACKGROUND Previous studies have shown that cocaine abusers have cerebral perfusion deficits that may diminish cognitive functioning. This study examined whether cocaine-dependent patients have perfusion abnormalities associated with poor decision-making ability as measured by the Iowa Gambling Task (IGT). METHODS Seventeen abstinent cocaine-dependent patients were administered the IGT after completion of resting 99mTc-HMPAO single-photon emission computed tomography (SPECT). RESULTS Better IGT performance was negatively correlated with perfusion within the anterior cingulate gyrus, middle frontal gyrus, medial frontal gyrus, and superior frontal gyrus. The time to complete card selections was positively correlated with the severity of impairment. CONCLUSIONS Resting hyperperfusion in brain regions previously implicated in decision making and response inhibition was associated with worse IGT scores. Impaired performance was related to a greater amount of time taken for card selections, suggesting that reduced ability was due to cognitive factors other than an impulsive response pattern.

Postoperative Cognitive Dysfunction: Minding the Gaps in Our Knowledge of a Common Postoperative Complication in the Elderly.

Postoperative cognitive dysfunction (POCD) is a common complication associated with significant morbidity and mortality in elderly patients. There is much interest in and controversy about POCD, reflected partly in the increasing number of articles published on POCD recently. Recent work suggests surgery may also be associated with cognitive improvement in some patients, termed postoperative cognitive improvement (POCI). As the number of surgeries performed worldwide approaches 250 million per year, optimizing postoperative cognitive function and preventing/treating POCD are major public health issues. In this article, we review the literature on POCD and POCI, and discuss current research challenges in this area.