Andrew T. DeMarco's research while affiliated with Georgetown University and other places

... Several methods have been developed for clustering, including spectral clustering and k-means [5][6][7], which rely on the metric of data similarity [8][9][10]. Due to the potential clustering representation, nonnegative matrix factorization, which is an effective method for data dimensionality reduction and feature extraction, has been widely used in clustering tasks [11,12]. NMF expresses part-based data by finding two nonnegative matrices whose product is close to the raw data and only allows additive combinations of data. ...

... Structural connectomes were constructed from the HARDI data through the MRtrix 3.0 software (Tournier et al. 2019) as described in full by Dickens et al. 2021. Briefly, preprocessing followed the standard stepwise application of gaussian noise removal, gibbs ringing artifact removal, correction of distortions induced by motion, eddy currents, and magnetic susceptibility, and correction of B0 field inhomogeneity. ...

... After a left hemisphere stroke in adulthood, language impairments tend to be chronic. In patients who have spared language areas in the left hemisphere, language processing continues to recruit these regions (however, see DeMarco et al., 2021), and in some cases also homotopic right hemisphere regions (Turkeltaub et al., 2011). Some studies have argued that better language outcomes after adult stroke depend on reengaging the intact left hemisphere tissue (see Anglade et al., 2014, andTurkeltaub, 2015, for a review of language recovery in the left versus right hemisphere after adult stroke). ...

... It is generally recognized that ischemic strokes affecting the dominant hemisphere, which in the majority of the population is the left-as opposed to the non-dominant or right hemisphere, can result in unique clinical syndromes and deficits depending on the regions affected. Multiple studies [74][75][76][77][78][79][80][81][82] have also shown that the right hemisphere's involvement is predictive of inferior functional outcomes in patients with AIS. Given that the connection between lesion location, clinical impairments, recovery and functional outcome, as measured by mRS, is expected to be altered by the site of the lesion, differentiation is crucial [82][83][84][85][86][87]. ...

... Whether due to the subtlety of de cits caused by cerebellar lesions or because of heterogeneity of cerebellar circuitry between individuals, various models and theories of cerebellar function with regards to cognition and speech have been proposed, but there is lack of conformity between experts on the localization of various nonmotor cerebellar functions 2,4 . This becomes increasingly important in an era when post-stroke management is introducing modalities such as transcranial direct current stimulation (tDCS) that can be targeted to certain areas of the brain to potentially improve recovery [5][6][7] . Formal correlation of cerebellar regions to elements of speech and neurocognition could have an exponential impact on the future management of stroke and other cerebellar lesions. ...

... The anterior and the dorsomedial prefrontal cortices exhibited a more robust response in value-based decisionmaking and profitable actions (45)(46)(47)(48). Moreover, the DLPFC activation was also observed during demanding problem-solving tasks, conscious self-monitoring, and focused attention (49)(50)(51). Given the association between DLPFC activity with the inhibition of pre-potent impulses (52) and the discouragement of declaration of individual thoughts (53), these two facts conform to the truthsuppression context. ...

... The result is a group or individual functional anomaly map (FAM). The method has been validated for studying cerebrovascular lesions and their effects on distant brain function, [23] and showed correlation with clinical symptoms and cognitive profiles. [24] For generating an individual FAM, time-varying rsfMRI signal aberrations are estimated by contrasting individual patient data to a group of controls in a support vector regression model ( Figure 1). ...

... Further, the language network is sensitive to linguistic regularities at all levels: from phonological/sub-lexical, to word level, to phrase/sentence level [Bautista and Wilson, 2016, Blank et al., 2016, Fedorenko et al., 2011 and supports linguistic operations that are related to both the processing of word meanings and those related to combinatorial semantic and syntactic processing , Hu et al., 2022b. This consistent recruitment for language across a broad range of conditions, as well as the fact that damage to the language network leads to linguistic deficits [e.g., Bates et al., 2003, Broca, 1865, Damasio, 1992, Mesulam, 2001, Mesulam et al., 2014, Saffran, 2000, Wernicke, 1874, Wilson et al., 2019, indicates that this set of regions stores our linguistic knowledge representations-a set of mappings between linguistic forms and meanings. ...

... In verbal working memory studies, it has been found that Broca's area, premotor areas (PMC), the STG, the inferior parietal lobule (IPL), the superior parietal lobule (SPL), insula, and the cerebellum are involved in the processing of verbal working memory (Paulesu et al., 1993;Awh et al., 1996;Fiez et al., 1996;Bamiou et al., 2003;Gruber and von Cramon, 2003;Crottaz-Herbette et al., 2004;Ravizza et al., 2004;Chen and Desmond, 2005;Kirschen et al., 2005;Koelsch et al., 2009;Cowan et al., 2011;Huang et al., 2013;Li et al., 2014;Fegen et al., 2015;Majerus et al., 2016;Emch et al., 2019;Ghaleh et al., 2020;Hoddinott et al., 2021). In the tonal working memory domain, relevant activations were found in the intra parietal sulcus (IPS), inferior frontal gyrus (IFG), cerebellum, dorsal lateral prefrontal cortex (DLPFC), supramarginal gyrus (SMG), STG, insula, and hippocampus (Zatorre et al., 1994;Holcomb et al., 1998;Griffiths et al., 1999;Gaab et al., 2003;Foster and Zatorre, 2010;Linke et al., 2011;Schulze et al., 2011;Albouy et al., 2013Albouy et al., , 2015Albouy et al., , 2017Foster et al., 2013;Kumar et al., 2016;Czoschke et al., 2021;Erhart et al., 2021). ...

... Given that emergent factors reflect the specific dataset, it is useful to review the range of tasks entered into PCA as well as how they load on particular factors. This is relevant to the refinement of language models and the study of neural substrates of language processing that use derived factors from PCA in conjunction with voxel-based lesion correlation maps (e.g., Mirman et al., 2015a,b;Woollams et al., 2018;Dickens et al., 2019;Ingram et al., 2020). ...