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Anisha Keshavan

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4 papers

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YNIMG Journal 2018 Journal Article

Human aging reduces the neurobehavioral influence of motivation on episodic memory

Maiya R. Geddes
Aaron T. Mattfeld
Carlo de los Angeles
Anisha Keshavan
John D.E. Gabrieli

The neural circuitry mediating the influence of motivation on long-term declarative or episodic memory formation is delineated in young adults, but its status is unknown in healthy aging. We examined the effect of reward and punishment anticipation on intentional declarative memory formation for words using an event-related functional magnetic resonance imaging (fMRI) monetary incentive encoding task in twenty-one younger and nineteen older adults. At 24-hour memory retrieval testing, younger adults were significantly more likely to remember words associated with motivational cues than neutral cues. Motivational enhancement of memory in younger adults occurred only for recollection (“remember” responses) and not for familiarity (“familiar” responses). Older adults had overall diminished memory and did not show memory gains in association with motivational cues. Memory encoding associated with monetary rewards or punishments activated motivational (substantia nigra/ventral tegmental area) and memory-related (hippocampus) brain regions in younger, but not older, adults during the target word periods. In contrast, older and younger adults showed similar activation of these brain regions during the anticipatory motivational cue interval. In a separate monetary incentive delay task that did not require learning, we found evidence for relatively preserved striatal reward anticipation in older adults. This supports a potential dissociation between incidental and intentional motivational processes in healthy aging. The finding that motivation to obtain rewards and avoid punishments had reduced behavioral and neural influence on intentional episodic memory formation in older compared to younger adults is relevant to life-span theories of cognitive aging including the dopaminergic vulnerability hypothesis.

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YNIMG Journal 2018 Journal Article

Mindcontrol: A web application for brain segmentation quality control

Anisha Keshavan
Esha Datta
Ian M. McDonough
Christopher R. Madan
Kesshi Jordan
Roland G. Henry

Tissue classification plays a crucial role in the investigation of normal neural development, brain-behavior relationships, and the disease mechanisms of many psychiatric and neurological illnesses. Ensuring the accuracy of tissue classification is important for quality research and, in particular, the translation of imaging biomarkers to clinical practice. Assessment with the human eye is vital to correct various errors inherent to all currently available segmentation algorithms. Manual quality assurance becomes methodologically difficult at a large scale - a problem of increasing importance as the number of data sets is on the rise. To make this process more efficient, we have developed Mindcontrol, an open-source web application for the collaborative quality control of neuroimaging processing outputs. The Mindcontrol platform consists of a dashboard to organize data, descriptive visualizations to explore the data, an imaging viewer, and an in-browser annotation and editing toolbox for data curation and quality control. Mindcontrol is flexible and can be configured for the outputs of any software package in any data organization structure. Example configurations for three large, open-source datasets are presented: the 1000 Functional Connectomes Project (FCP), the Consortium for Reliability and Reproducibility (CoRR), and the Autism Brain Imaging Data Exchange (ABIDE) Collection. These demo applications link descriptive quality control metrics, regional brain volumes, and thickness scalars to a 3D imaging viewer and editing module, resulting in an easy-to-implement quality control protocol that can be scaled for any size and complexity of study.

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YNIMG Journal 2016 Journal Article

Intra- and interscanner variability of magnetic resonance imaging based volumetry in multiple sclerosis

Viola Biberacher
Paul Schmidt
Anisha Keshavan
Christine C. Boucard
Ruthger Righart
Philipp Sämann
Christine Preibisch
Daniel Fröbel

Brain volumetric measurements in multiple sclerosis (MS) reflect not only disease-specific processes but also other sources of variability. The latter has to be considered especially in multicenter and longitudinal studies. Here, we compare data generated by three different 3-Tesla magnetic resonance scanners (Philips Achieva; Siemens Verio; GE Signa MR750). We scanned two patients diagnosed with relapsing remitting MS six times per scanner within three weeks (T1w and FLAIR, 3D). We assessed T2-hyperintense lesions by an automated lesion segmentation tool and determined volumes of grey matter (GM), white matter (WM) and whole brain (GM+WM) from the lesion-filled T1-weighted images using voxel-based morphometry (SPM8/VBM8) and SIENAX (FSL). We measured cortical thickness using FreeSurfer from both, lesion-filled and original T1-weighted images. We quantified brain volume changes with SIENA. In both patients, we found significant differences in total lesion volume, global brain tissue volumes and cortical thickness measures between the scanners. Morphometric measures varied remarkably between repeated scans at each scanner, independent of the brain imaging software tool used. We conclude that for cross-sectional multicenter studies, the effect of different scanners has to be taken into account. For longitudinal monocentric studies, the expected effect size should exceed the size of false positive findings observed in this study. Assuming a physiological loss of brain volume of about 0. 3% per year in healthy adult subjects (Good et al. , 2001), which may double in MS (De Stefano et al. , 2010; De Stefano et al. , 2015), with current tools reliable estimation of brain atrophy in individual patients is only possible over periods of several years.

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YNIMG Journal 2016 Journal Article

Power estimation for non-standardized multisite studies

Anisha Keshavan
Friedemann Paul
Mona K. Beyer
Alyssa H. Zhu
Nico Papinutto
Russell T. Shinohara
William Stern
Michael Amann

A concern for researchers planning multisite studies is that scanner and T1-weighted sequence-related biases on regional volumes could overshadow true effects, especially for studies with a heterogeneous set of scanners and sequences. Current approaches attempt to harmonize data by standardizing hardware, pulse sequences, and protocols, or by calibrating across sites using phantom-based corrections to ensure the same raw image intensities. We propose to avoid harmonization and phantom-based correction entirely. We hypothesized that the bias of estimated regional volumes is scaled between sites due to the contrast and gradient distortion differences between scanners and sequences. Given this assumption, we provide a new statistical framework and derive a power equation to define inclusion criteria for a set of sites based on the variability of their scaling factors. We estimated the scaling factors of 20 scanners with heterogeneous hardware and sequence parameters by scanning a single set of 12 subjects at sites across the United States and Europe. Regional volumes and their scaling factors were estimated for each site using Freesurfer's segmentation algorithm and ordinary least squares, respectively. The scaling factors were validated by comparing the theoretical and simulated power curves, performing a leave-one-out calibration of regional volumes, and evaluating the absolute agreement of all regional volumes between sites before and after calibration. Using our derived power equation, we were able to define the conditions under which harmonization is not necessary to achieve 80% power. This approach can inform choice of processing pipelines and outcome metrics for multisite studies based on scaling factor variability across sites, enabling collaboration between clinical and research institutions.

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