Measuring language recovery in the underlying large‐scale neural network: Pulling together in the face of adversity

Language is an essential higher cognitive function. Aphasia following acute or progressive neurological damage always produces significant disability not only for patients’ professional lives but also for everyday activities. Aphasia in the context of stroke is relatively common, with estimates suggesting that up to 1=3 of acute patients present with language impairment, dropping to 20% in the chronic phase. Likewise in recent years there has been an ever-increasing recognition of language impairment in neurodegenerative disorders, not only as the presenting symptom in the context of the primary progressive aphasias, but also as a part of the symptom complex in many other types of dementia As these epidemiological figures would suggest, it is common to find that aphasic patients show at least some degree of language recovery. This is true not only in the very acute phase but also several months after stroke, albeit with the rate of change decreasing over time. This begs the obvious questions of which neural regions support such partial recovery and what the underlying mechanisms are. To answer, we must understand not only the neuroanatomy of normal language function but also how this supports recovery, with the potential of using behavioral and pharmacological interventions to promote intrinsic recovery processes, thereby achieving maximal language potential and thus minimizing disability. In the classical, modular view of language function, recovery was assumed to reflect either the return of partial function within the remnants of the affected dominant language region or the uptake of function within other homologous regions. We are now beginning to see radical changes in this classical view. The emerging contemporary perspective is very different in form and nature, and is a reflection of novel insights that arise from modern neuroscience techniques, with the different methods and analyses that they afford. Three key observations include: (1) that the adult, mature brain is not a fixed, rigid entity but has the capacity for plasticity-related changes; (2) that higher cognitive function, including language, reflects the joint action of a distributed, neural network; and (3) that recovery of function can reflect altered computation in the remaining nodes within this neural network and also a shift in the division of labor across this yoked set of neural regions. In their sophisticated investigation, Sharp et al studied a series of stroke patients with chronic, partially recovery language function using a combination of neuropsychological assessments and functional neuroimaging. Although such dual investigations are still all too rare, Sharp et al included two new elements that advance our understanding of the neural basis and mechanisms of recovered comprehension function. Rather than simply comparing the pattern of activation found in the patients and their matched controls, Sharp and colleagues investigated the changes in cohesion (functional connectivity) between the cortical regions that support language comprehension. Furthermore, they compared these data not only against neurologically intact control participants, but also against results collected from the same people while performing a task under challenging conditions (comprehension of a degraded input). The latter, novel methodology has been used previously in behavioral-only studies (to mimic aspects of patients’ performance) but, when combined with functional neuroimaging, it licenses a comparison between the changes observed in patients and the intrinsic changes found in neurologically intact brain function under nonoptimal conditions. Two key and important insights arise from this study. The first is that recovery of comprehension in the patients was related to increased, joint activity in the neural network: specifically, recovered comprehension performance aligned with increased functional connectivity between angular gyrus (AG) and the superior frontal gyrus (SFG), as well as between AG and the inferior temporal region. Second, these same functionally related