Sleepy without stimulation: subjective and objective sleepiness in actigraphy-verified natural short sleepers.

Natural short sleepers (NSS)-individuals who report minimal sleepiness or daytime dysfunction despite habitually sleeping less than the recommended amount (i.e., <7 h)-are a focus of growing interest in sleep research. Yet, the predominance of research on NSS has relied on subjective reports of functionality. The present study examined subjective and objective sleepiness among actigraphy-verified NSS in comparison with recommended (7-9 h/day) length sleepers (RLS) who reported similarly minimal daytime dysfunction. The study tested the hypothesis that under conditions of low environmental stimulation, NSS have increased risk of drowsiness and sleep onset, regardless of perceived alertness. The NSS and RLS groups were identified via screening and verified with a 14 day assessment with actigraphy, sleep diaries, and morning ratings of sleep restoration. In-laboratory resting electroencephalography (EEG) data were analysed using a computerised EEG-based algorithm (Vigilance Algorithm Leipzig; VIGALL) to classify second-by-second changes in objective sleepiness ranging from cognitively active alertness to sleep onset. Results demonstrated that NSS exhibited significantly higher drowsiness and sleep onset ('microsleeps') across 15 min of resting EEG despite perceptions of lower subjective sleepiness compared to RLS. Findings suggest that irrespective of perceived sleep restoration and alertness, NSS appear to be at high risk of objective sleepiness that is rapidly unmasked under conditions of low environmental stimulation. Such apparent discrepancy between subjective and objective sleepiness has potentially important public health implications. Future research directions, including tests of mechanisms and tailored sleep extension intervention, are discussed.

Neurite outgrowth deficits caused by rare PLXNB1 mutation in pediatric bipolar disorder.

Pediatric bipolar disorder (PBD) is a severe mood dysregulation condition that affects 0.5-1% of children and teens in the United States. It is associated with recurrent episodes of mania and depression and an increased risk of suicidality. However, the genetics and neuropathology of PBD are largely unknown. Here, we used a combinatorial family-based approach to characterize cellular, molecular, genetic, and network-level deficits associated with PBD. We recruited a PBD patient and three unaffected family members from a family with a history of psychiatric illnesses. Using resting-state functional magnetic resonance imaging (rs-fMRI), we detected altered resting-state functional connectivity in the patient as compared to an unaffected sibling. Using transcriptomic profiling of patient and control induced pluripotent stem cell (iPSC)-derived telencephalic organoids, we found aberrant signaling in the molecular pathways related to neurite outgrowth. We corroborated the presence of neurite outgrowth deficits in patient iPSC-derived cortical neurons and identified a rare homozygous loss-of-function PLXNB1 variant (c.1360C>C; p.Ser454Arg) responsible for the deficits in the patient. Expression of wild-type PLXNB1, but not the variant, rescued neurite outgrowth in patient neurons, and expression of the variant caused the neurite outgrowth deficits in cortical neurons from PlxnB1 knockout mice. These results indicate that dysregulated PLXNB1 signaling may contribute to an increased risk of PBD and other mood dysregulation-related disorders by disrupting neurite outgrowth and functional brain connectivity. Overall, this study established and validated a novel family-based combinatorial approach for studying cellular and molecular deficits in psychiatric disorders and identified dysfunctional PLXNB1 signaling and neurite outgrowth as potential risk factors for PBD.

Test-retest reliability of FreeSurfer-derived volume, area and cortical thickness from MPRAGE and MP2RAGE brain MRI images.

Background and purpose: Large MRI studies often pool data gathered from widely varying imaging sequences. Pooled data creates a potential source of variation in structural analyses which may cause misinterpretation of findings. The purpose of this study is to determine if data acquired using different scan sequences, head coils and scanners offers consistent structural measurements. Materials and methods: Participants (163 right-handed males: 82 typically developing controls, 81 participants with autism spectrum disorder) were scanned on the same day using an MPRAGE sequence with a 12-channel headcoil on a Siemens 3T Trio scanner and an MP2RAGE sequence with a 64-channel headcoil on a Siemens 3T Prisma scanner. Segmentation was performed using FreeSurfer to identify regions exhibiting variation between sequences on measures of volume, surface area, and cortical thickness. Intraclass correlation coefficient (ICC) and mean percent difference (MPD) were used as test-retest reproducibility measures. Results: ICC for total brain segmented volume yielded a 0.99 intraclass correlation, demonstrating high overall volumetric reproducibility. Comparison of individual regions of interest resulted in greater variation. Volumetric variability, although low overall, was greatest in the entorhinal cortex (ICC = 0.71), frontal (ICC = 0.60) and temporal (ICC = 0.60) poles. Surface area variability was greatest in the insula (ICC = 0.65), temporal (ICC = 0.64) and frontal (ICC = 0.68) poles. Cortical thickness was most variable in the frontal (ICC = 0.41) and temporal (ICC = 0.35) poles. Conclusion: Data collected on different scanners and head coils using MPRAGE and MP2RAGE are generally consistent for surface area and volume estimates. However, regional variability may constrain accuracy in some regions and cortical thickness measurements exhibit higher generalized variability.

"Fully automated segmentation of the corticospinal tract using the TractSeg algorithm in patients with brain tumors".

OBJECTIVE: Tractography has been used to define the presurgical location of white matter tracts, but this is subjective and time-intensive, making incorporation to imaging workflow at scale problematic. The objective is to validate a fully automated pipeline using the TractSeg algorithm (Wasserthal et al. NeuroImage 2018;183:239-253) to segment the corticospinal tract in patients with brain tumors adjacent to the corticospinal tract. METHODS: The process of importing a structural MPRAGE sequence and raw diffusion weighted images from PACS, executing the TractSeg algorithm, overlaying the resulting bilateral corticospinal tracts on the MPRAGE image, and exporting this composite image to PACS was automated. This procedure was used to segment the corticospinal tract in 28 patients with brain masses adjacent to or displacing the corticospinal tract. These segmentations were compared with both manual deterministic tractography performed with DSI Studio using seeds placed in the pons and an automated tractography method in DSI Studio. RESULTS: The automated algorithm was able to segment the bilateral corticospinal tracts in all 28 patients whereas the manual reference method and DSI Studio based automated tractography were unsuccessful in 2 and 1 patients, respectively. In all cases, the TractSeg segmentations very closely matched the manual segmentations. Also, TractSeg appeared to include larger portions of the lateral corticospinal tract fibers than the other 2 methods. CONCLUSION: The TractSeg algorithm demonstrated robust performance in segmenting the corticospinal tract in patients with brain tumors adjacent to this tract. The algorithm is fast to perform and has great potential for optimizing and streamlining neurosurgical planning.

Predicting biomarkers in intact older adults and those with amnestic Mild Cognitive Impairment, and mild Alzheimer's Disease using the Repeatable Battery for the Assessment of Neuropsychological Status.

INTRODUCTION: The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) has been associated, to varying degrees, with commonly used biomarkers of Alzheimer's disease (AD). Given the ease of RBANS administration as a screening tool for clinical trials and other applications, a better understanding of how RBANS performance is associated with presence of APOE ε4 allele[s], cerebral amyloid burden, and hippocampal volume is warranted. METHOD: One hundred twenty-one older adults who were classified as intact, amnestic Mild Cognitive Impairment, or mild AD underwent cognitive assessment with the RBANS, genetic analysis, and quantitative brain imaging. APOE ε4 carrier status, 18F-Flutemetamol composite standardized uptake value ratio (SUVR), and hippocampal volume were each regressed on demographic variables and RBANS Total Scale score, Index scores, and subtest scores. RESULTS: Lower RBANS Total Scale score or Delayed Memory Index (DMI) predicted the presence of APOE ε4 allele[s], higher cerebral amyloid burden, and lower hippocampal volumes. DMI was a slightly better predictor than Total Scale score for most AD biomarkers. No demographic variables consistently contributed to these models. CONCLUSIONS: The RBANS - DMI in particular - is sensitive to AD pathology. As such, it could be used as a predictive tool, particularly in clinical drug trials to enrich samples prior to less accessible AD biomarker investigation.

Left Ear Hearing Predicts Functional Activity in the Brains of Patients with Alzheimer's Disease Dementia.

OBJECTIVES: To determine whether central speech processing ability, as measured by hearing in noise, differs between right and left ears in adults with Alzheimer's disease related dementia (AD) as well as whether differences in central speech processing ability correlate with an fMRI-based measurement of global functional brain connectivity. METHODS: This prospective study was carried out at a tertiary referral center. Patients with an AD diagnosis and pure tone averages 40 dB HL or better were included. They were examined using resting-state fMRI and underwent central audiometric testing using the Dichotic Sentence Identification Test (DSI), the Dichotic Digits Test (DD), and the Synthetic Sentence Identification Test (SS), which test hearing in noise. DSI scores were correlated with resting-state fMRI connectivity between 361 distinct gray matter brain regions of interest (ROIs). Average global connectivity was calculated as mean functional connectivity between an ROI and the other 360 regions, a quantitative marker representing overall functional connectivity in the brain. RESULTS: Sixteen subjects had adequate fMRI and hearing data. The average age was 71.5 years old (±6.0). The average DSI score for the left ear was 40% (±34%) compared to 90% (±10%) in the right ear (P < .001). No difference between ears was noted on the DD. SS does not differentiate between ears, but worsening scores were noted with increasing background noise. Of the fMRI ROIs, 269 of the 361 had multiple comparison corrected significant correlations between global connectivity and DSI of the left ear (P = .004, r = .673), and all 269 showed higher functional connectivity for individuals with higher left DSI score. No correlations between DSI of the right ear and functional connectivity were found. CONCLUSIONS: Correlation was noted between left sided DSI and functional connectivity in patients with AD. Auditory input from the left ear was more susceptible to impairment, suggesting that side-specific auditory input may influence central auditory processing.

Changes in the Auditory Association Cortex in Dementing Illnesses.

OBJECTIVE: To evaluate the relationship between degree of cognitive impairment and gray-matter density changes in the auditory cortex. STUDY DESIGN: Retrospective case-control. PATIENTS: Six hundred sixty-three patients of a tertiary referral center cognitive disorders clinic. INTERVENTION: Magnetic resonance imaging. MAIN OUTCOME MEASURES: Ratios of gray matter density of the primary auditory cortex (A1) to whole brain and auditory association cortex (AAC) to whole brain in patients with Alzheimer's disease (AD) compared with mild cognitive impairment (MCI) and patients with a mini-mental state exam (MMSE) scores ≤25 versus >25. RESULTS: After multivariate analysis, a statistically significant difference between AAC to brain ratios for patients with a MMSE ≤25 (n = 325) compared with >25 (n = 269) was found, with values -0.03 (95% CI -0.04 to -0.02, p < 0.0001) on the left and -0.04 (95% CI -0.06 to -0.03, p < 0.0001) on the right. The adjusted average difference of left and right AAC to brain ratios between AD patients (n = 218) compared with MCI patients (n = 121) was also statistically significant, at -0.03 (95% CI -0.05 to -0.01, p = 0.004) and -0.05 (95% CI -0.07 to -0.03, p < 0.0001), respectively. There was no statistically significant difference in the left or right A1 to brain ratios between the MMSE groups or between the AD and MCI groups. CONCLUSIONS: The AAC for patients with MMSE ≤25 and for those with AD shows decreased gray matter density when compared with patients with better cognitive function. No difference was detected in A1, raising the possibility that patients may have intact neural hearing, but impaired ability to interpret sounds.

Understanding heterosexual women's erotic flexibility: the role of attention in sexual evaluations and neural responses to sexual stimuli.

Many women experience desires, arousal and behavior that run counter to their sexual orientation (orientation inconsistent, 'OI'). Are such OI sexual experiences cognitively and neurobiologically distinct from those that are consistent with one's sexual orientation (orientation consistent, 'OC')? To address this question, we employed a mindful attention intervention-aimed at reducing judgment and enhancing somatosensory attention-to examine the underlying attentional and neurobiological processes of OC and OI sexual stimuli among predominantly heterosexual women. Women exhibited greater neural activity in response to OC, compared to OI, sexual stimuli in regions associated with implicit visual processing, volitional appraisal and attention. In contrast, women exhibited greater neural activity to OI, relative to OC, sexual stimuli in regions associated with complex visual processing and attentional shifting. Mindfully attending to OC sexual stimuli reduced distraction, amplified women's evaluations of OC stimuli as sexually arousing and deactivated the superior cerebellum. In contrast, mindfully attending to OI sexual stimuli amplified distraction, decreased women's evaluations of OI stimuli as sexually arousing and augmented parietal and temporo-occipital activity. Results of the current study constrain hypotheses of female erotic flexibility, suggesting that sexual orientation may be maintained by differences in attentional processing that cannot be voluntarily altered.

Functional connectivity of emotional well-being: Overconnectivity between default and attentional networks is associated with attitudes of anger and aggression.

Functional MRI connectivity has identified neurophysiology relevant to cognition and personality, motivating a search for relationships between brain architecture and emotional health and well-being. Two approaches were used to asses functional connectivity correlates of emotional health and well-being. The first approach used principal component analysis to evaluate resting-state functional magnetic resonance imaging data from the Human Connectome Project 1200 Subjects Data Release. Pairwise functional connectivity measurements were obtained from a 5 mm resolution parcellation of brain gray matter. Principal components were calculated for each individual and for group mean connectivity data and compared to obtain an estimate of typicality of functional connectivity for each component in each subject. Typicality scores were compared to reported emotional health metrics using a general linear model. The second approach calculated functional connectivity between each pair of networks from a 17-resting-state network cortical parcellation. Typicality of connectivity showed significant correlation across the population to emotional metrics corresponding to attitudes of anger and aggression in 3 of 10 principal components. Additionally, functional connectivity between the default and attentional networks was positively correlated with scores of attitudes of anger and aggression. These findings are consistent with a mechanism of impaired effortful control and decreased response inhibition of impulsivity.

Generalizability and reproducibility of functional connectivity in autism.

Background: Autism is hypothesized to represent a disorder of brain connectivity, yet patterns of atypical functional connectivity show marked heterogeneity across individuals. Methods: We used a large multi-site dataset comprised of a heterogeneous population of individuals with autism and typically developing individuals to compare a number of resting-state functional connectivity features of autism. These features were also tested in a single site sample that utilized a high-temporal resolution, long-duration resting-state acquisition technique. Results: No one method of analysis provided reproducible results across research sites, combined samples, and the high-resolution dataset. Distinct categories of functional connectivity features that differed in autism such as homotopic, default network, salience network, long-range connections, and corticostriatal connectivity, did not align with differences in clinical and behavioral traits in individuals with autism. One method, lag-based functional connectivity, was not correlated to other methods in describing patterns of resting-state functional connectivity and their relationship to autism traits. Conclusion: Overall, functional connectivity features predictive of autism demonstrated limited generalizability across sites, with consistent results only for large samples. Different types of functional connectivity features do not consistently predict different symptoms of autism. Rather, specific features that predict autism symptoms are distributed across feature types.

Neural reward processing in self-reported short sleepers: examination of gambling task brain activation in the Human Connectome Project database.

STUDY OBJECTIVES: Much of what we assume about the effects of short sleep duration on neural reward processing derives from total sleep deprivation studies. Although total sleep deprivation appears rare, habitual short sleep is common: 30% of working US adults report habitually sleeping ≤ 6 hours/night. It remains largely unknown whether habitual short sleepers exhibit similar reward processing brain activation patterns to those observed following total sleep deprivation in prior studies. Therefore, our aim was to test objectively reward processing brain activation patterns associated with self-reported habitual short sleep duration in a large sample. METHODS: Nine hundred and fifty-two adult participants from the Human Connectome Project database were grouped on reported habitual short (≤6 hours) vs. medium-length (7-9 hours) sleep duration using the Pittsburgh Sleep Quality Index (PSQI). Reward processing brain activation was examined using a gambling task during functional magnetic resonance imaging (fMRI). Subject-level covariates for age, sex, continuous sleep duration, daytime dysfunction, and PSQI total score are provided as supplemental analyses. RESULTS: Brain activation patterns revealed expected reward processing-related activation for age and sex. However, activation for sleep duration, dysfunction, and PSQI score did not correspond to those evident in previous total sleep deprivation studies. CONCLUSIONS: Self-reported short sleep duration, perceived sleep-related dysfunction, and sleep quality via PSQI do not appear to be meaningfully associated with activation in well-described regions of the human neurobiological reward circuit. As these findings are counter to prior results using experimental sleep deprivation, future work focused on more direct comparisons between self-reported sleep variables and experimental sleep deprivation appears warranted.

Neuroimaging in Psychiatry and Neurodevelopment: why the emperor has no clothes.

Neuroimaging has been a dominant force in guiding research into psychiatric and neurodevelopmental disorders for decades, yet researchers have been unable to formulate sensitive or specific imaging tests for these conditions. The search for neuroimaging biomarkers has been constrained by limited reproducibility of imaging techniques, limited tools for evaluating neurochemistry, heterogeneity of patient populations not defined by brain-based phenotypes, limited exploration of temporal components of brain function, and relatively few studies evaluating developmental and longitudinal trajectories of brain function. Opportunities for development of clinically impactful imaging metrics include longer duration functional imaging data sets, new engineering approaches to mitigate suboptimal spatiotemporal resolution, improvements in image post-processing and analysis strategies, big data approaches combined with data sharing of multisite imaging samples, and new techniques that allow dynamical exploration of brain function across multiple timescales. Despite narrow clinical impact of neuroimaging methods, there is reason for optimism that imaging will contribute to diagnosis, prognosis, and treatment monitoring for psychiatric and neurodevelopmental disorders in the near future.

External validation of change formulae in neuropsychology with neuroimaging biomarkers: A methodological recommendation and preliminary clinical data.

OBJECTIVE: Within neuropsychology, a number of mathematical formulae (e.g. reliable change index, standardized regression based) have been used to determine if change across time has reliably occurred. When these formulae have been compared, they often produce different results, but 'different' results do not necessarily indicate which formulae are 'best.' The current study sought to further our understanding of change formulae by comparing them to clinically relevant external criteria (amyloid deposition and hippocampal volume). METHOD: In a sample of 25 older adults with varying levels of cognitive intactness, participants were tested twice across one week with a brief cognitive battery. Seven different change scores were calculated for each participant. An amyloid PET scan (to get a composite of amyloid deposition) and an MRI (to get hippocampal volume) were also obtained. RESULTS: Deviation-based change formulae (e.g. simple discrepancy score, reliable change index with or without correction for practice effects) were all identical in their relationship to the two neuroimaging biomarkers, and all were non-significant. Conversely, regression-based change formulae (e.g. simple and complex indices) showed stronger relationships to amyloid deposition and hippocampal volume. CONCLUSIONS: These results highlight the need for external validation of the various change formulae used by neuropsychologists in clinical settings and research projects. The findings also preliminarily suggest that regression-based change formulae may be more relevant than deviation-based change formulae in this context.

Autism Classification Using Topological Features and Deep Learning: A Cautionary Tale.

The identification of autistic individuals using resting state functional connectivity networks can provide an objective diagnostic method for autism spectrum disorder (ASD). The present state-of-the-art machine learning model using deep learning has a classification accuracy of 70.2% on the ABIDE (Autism Brain Imaging Data Exchange) data set. In this paper, we explore the utility of topological features in the classification of ASD versus typically developing control subjects. These topological features have been shown to provide a complementary source of discriminative information in applications such as 2D object classification and social network analysis. We evaluate the performance of three different representations of topological features - persistence diagrams, persistence images, and persistence landscapes - for autism classification using neural networks, support vector machines and random forests. We also propose a hybrid approach of augmenting topological features with functional correlations, which typically outperforms the models that use functional correlations alone. With this approach, even with a simple 3-layer neural network, we are able to achieve a classification accuracy of 69.2% on the ABIDE data set. However, our experiments also show that the improvement due to topological features is not always statistically significant. Therefore, we offer a cautionary tale to the practitioners regarding the limited discriminative power of topological features derived from fMRI data for the classification of autism.

Beery VMI and Brain Volumetric Relations in Autism Spectrum Disorder.

Although diminished proficiency on tasks that require visual-motor integration (VMI) has been reported in individuals with autism spectrum disorder (ASD), very few studies have examined the association between VMI performance and neuroanatomical regions of interest (ROI) involved in motor and perceptual functioning. To address these issues, the current study included an all-male sample of 41 ASD (ages 3-23 years) and 27 typically developing (TD) participants (ages 5-26 years) who completed the Beery-Buktenica Developmental Test of Visual-Motor Integration (Beery VMI) as part of a comprehensive neuropsychological battery. All participants underwent 3.0 T magnetic resonance imaging (MRI) with image quantification (FreeSurfer software v5.3). The groups were statistically matched on age, handedness, and intracranial volume (ICV). ASD participants performed significantly lower on VMI and IQ measures compared with the TD group. VMI performance was significantly correlated with FSIQ and PIQ in the TD group only. No pre-defined neuroanatomical ROIs were significantly different between groups. Significant correlations were observed in the TD group between VMI and total precentral gyrus gray matter volume (r = .51, p = .006) and total frontal lobe gray matter volume (r = .46, p = .017). There were no significant ROI correlations with Beery VMI performance in ASD participants. At the group level, despite ASD participants exhibiting reduced visuomotor abilities, no systematic relation with motor or sensory-perceptual ROIs was observed. In the TD group, results were consistent with the putative role of the precentral gyrus in motor control along with frontal involvement in planning, organization, and execution monitoring, all essential for VMI performance. Given that similar associations between VMI and ROIs were not observed in those with ASD, neurodevelopment in ASD group participants may not follow homogenous patterns making correlations in these brain regions unlikely to be observed.

Surface-Based Spatial Pyramid Matching of Cortical Regions for Analysis of Cognitive Performance.

We propose a method to analyze the relationship between the shape of functional regions of the cortex and cognitive measures, such as reading ability and vocabulary knowledge. Functional regions on the cortical surface can vary not only in size and shape but also in topology and position relative to neighboring regions. Standard diffeomorphism-based shape analysis tools do not work well here because diffeomorphisms are unable to capture these topological differences, which include region splitting and merging across subjects. State-of-the-art cortical surface shape analyses compute derived regional properties (scalars), such as regional volume, cortical thickness, curvature, and gyrification index. However, these methods cannot compare the full extent of topological or shape differences in cortical regions. We propose icosahedral spatial pyramid matching (ISPM) of region borders computed on the surface of a sphere to capture this variation in regional topology, position, and shape. We then analyze how this variation corresponds to measures of cognitive performance. We compare our method to other approaches and find that it is indeed informative to consider aspects of shape beyond the standard approaches. Analysis is performed using a subset of 27 test/retest subjects from the Human Connectome Project in order to understand both the effectiveness and reproducibility of this method.

High-Order Visual Processing, Visual Symptoms, and Visual Hallucinations: A Possible Symptomatic Progression of Parkinson's Disease.

Objective: To determine whether Parkinson disease (PD) patients with (VH) have different clinical characteristics and gray-matter volume than those with visual misperceptions (VM) or other visual symptoms (OvS). Background: The spectrum of visual complaints in PD is broad and complex. Methods: We conducted a retrospective chart review of 525 PD patients to identify the frequency of visual symptoms and the association with clinical and radiological features. Brain volumetric MRI data was analyzed using multivariate logistic regression to differentiate cases with and without visual symptoms. Results: Among 525 PD cases, visual complaints were documented in 177 (33.7%). Among these, 83 (46.9%) had VH, 31 (17.5%) had VM, and 63 (35.6%) had OvS (diplopia, blurry vision, photophobia, dry eyes, and eye pain or soreness). When compared to OvS, patients with VH had significantly higher age, duration of disease, rate of REM sleep behavior disorder, and cognitive impairment. Visual hallucinations patients had decreased age-adjusted volumetric averages in 28/30 gray-matter regions when compared to PD without visual symptoms and 30/30 gray-matter regions when compared to VM patients. Conclusions: Visual symptoms in PD may represent a spectrum from OvS to VM to VH, with progression of the latter associated with older age, duration of disease, presence of REM sleep behavior disorder, cognitive impairment, and decreased gray-matter volume.

Sustained versus instantaneous connectivity differentiates cognitive functions of processing speed and episodic memory.

Synchrony of brain activity over time describes the functional connectivity between brain regions but does not address the temporal component of this relationship. We propose a complementary method of analysis by introducing the width of cross-correlation curves between functional MRI (fMRI) time series as a metric of the relative duration of synchronous activity between brain regions, or "sustained connectivity". Using resting-state fMRI, cognitive, and demographics data from 1,003 subjects included in the Human Connectome Project, we find that sustained connectivity is a reproducible trait in individuals, heritable, more transient in females, and shows changes with age in early adulthood. Sustained connectivity in sensory brain regions is specifically associated with differences in processing speed across subjects, particularly in men. In contrast, traditional functional connectivity was correlated with a measure of episodic memory, but not with processing speed. Individual differences in hemodynamic response function (HRF) are closely approximated by sustained connectivity and width of the HRF is also correlated with processing speed across individuals, suggesting that variability in hemodynamic response may be influenced by transient versus sustained neural activity rather than simply differences in vascularity and signal transduction. Sustained connectivity may provide new opportunities to study brain dynamics in clinical populations.

Short-term repeat cognitive testing and its relationship to hippocampal volumes in older adults.

BACKGROUND: Practice effects are improvements in cognitive test scores due to repeated exposure to testing materials. If practice effects provide information about Alzheimer's disease pathology, then they could be useful for clinical trials enrichment. The current study sought to add to the limited literature on short-term practice effects on cognitive tests and their relationship to neuroimaging biomarkers. METHODS: Twenty-five, non-demented older adults (8 cognitively intact, 17 with mild cognitive impairment) received magnetic resonance imaging and two testing sessions across one week to determine practice effects on seven neuropsychological test scores. A series of correlations examined if hippocampal volume was associated with baseline, one-week, or practice effects scores on these tests. Next, a series of stepwise multiple regression models examined which of the three test scores best predicted hippocampal volumes RESULTS: In the correlation analysis, baseline scores on 5 of the 7 tests were significantly associated with hippocampal volumes, one week scores were significantly related for 7 of the 7 tests, and practice effects scores were significantly correlated for 4 of the 7 tests. In the stepwise regression models, 5 of the 7 tests indicated that one-week scores best predicted hippocampal volumes. For the other models, baseline score and practice effects score each best predicted hippocampal volume. CONCLUSIONS: These results add to the growing body of evidence suggesting that diminished practice effects on short-term repeat testing is related to neuroimaging biomarkers of Alzheimer's disease and may serve as a screening tool for clinical practice and to enrich samples for research trials.