Performance-based connectivity analysis: A path to convergence with clinical studies

Abstract

Connectivity analyses have become increasingly important in functional imaging. When used to describe the functional anatomy of a specific behavior, these analyses are generally applied to a subset of the data that demonstrate significant differences when experimental conditions are contrasted. Such data reduction is sub-optimal for a systems approach as it assumes that all data that survive the statistical contrast filter are related to the behavior and that none of the filtered data have a significant function. When such data filtering is applied to speech and language tasks, the resulting functional anatomy rarely reflects the brain lateralization established in over a century and a half of clinical studies. A two-step performance-based connectivity analysis is described in which the first step uses multiple linear regression to establish a direct relationship between regional brain activity and a measure of performance. The second step uses partial correlations to examine the functional relationships between the predictor regions and other brain regions. When applied to regional cerebral blood flow data obtained with positron emission tomography during a speech production task, the results demonstrate left lateralization of motor control areas, thalamic involvement in repetition rate, and auditory cortical suppression, all consistent with clinical observations. The integration of performance measures into the earliest stages of image analysis without reliance on data filtering based on decomposition may provide a path towards convergence with traditional descriptions of functional anatomy based on clinical studies.

Attentional load modulates large-scale functional brain connectivity beyond the core attention networks

“…While the right and left hemisphere dorsal visual stream (IC27 and IC37, respectively) both showed increased signal amplitude in response to tracking load, they showed a strong decoupling in response to tracking. This supports that the amplitude of brain responses does not necessarily reflect their functional significance (Sidtis, 2012), and investigating large-scale connectivity during task engagement provides a highly useful supplement to conventional contrast based analyses.…”

Neural correlates of dystonic tremor: a multimodal study of voice tremor in spasmodic dysphonia

“…This region is known to be involved in an array of preparatory processes to speech motor execution, including sound categorization, discrimination and phonological decisions on sound content of words (Burton et al 2005; Husain et al 2006), processing of syllabic order (Moser et al 2009), semantic retrieval (Rodd et al 2005; Noppeney and Price 2002), covert articulatory planning and translating speech into articulatory code (Paulesu et al 1997; Sidtis 2012). In our patient cohorts, coupling between functional and structural abnormalities in the IFG may reflect symptomatology of SD and SD/DTv, which is associated with continuous struggles with the choice of words in order to avoid or minimize SD-characteristic voice breaks and to accurately map articulatory planning to speech motor output due to both voice breaks and tremor.…”

Speech networks at rest and in action: interactions between functional brain networks controlling speech production

“…The hemispheric dominance during speech and language production has been a well-known, albeit continuously debated, phenomenon (Findlay et al 2012;Hickok and Poeppel 2007;Lindell 2006;Peelle 2012;Ramsey et al 2001;Rauschecker and Scott 2009;Riecker et al 2000Riecker et al , 2002Sidtis 2012;Wildgruber et al 1996), which may follow a neurodevelopmental process of progressive maturation of intrahemispheric functional connectivity and language exposure (Dehaene-Lambertz et al 2002;Dubois et al 2006;Perani et al 2011). We and others have shown that the sensorimotor, prefrontal, and striatal functional networks controlling speech production are left lateralized (Bourguignon 2014;Gehrig et al 2012;Kell et al 2011;Manes et al 2014;Morillon et al 2010;Simonyan et al , 2013.…”