Multivariate Approaches to Understanding Aphasia and its Neural Substrates

Symptomatology after damage to the angular gyrus through the lenses of modern lesion-symptom mapping

Brain-behavior relationships are complex. For instance, one might know a brain region's function(s) but still be unable to accurately predict deficit type or severity after damage to that region. Here, I discuss the case of damage to the angular gyrus (AG) that can cause left-right confusion, finger agnosia, attention deficit, and lexical agraphia, as well as impairment in sentence processing, episodic memory, number processing, and gesture imitation. Some of these symptoms are grouped under AG syndrome or Gerstmann's syndrome, though its exact underlying neuronal systems remain elusive. This review applies recent frameworks of brain-behavior modes and principles from modern lesion-symptom mapping to explain symptomatology after AG damage. It highlights four major issues for future studies: (1) functionally heterogeneous symptoms after AG damage need to be considered in terms of the degree of damage to (i) different subdivisions of the AG, (ii) different AG connectivity profiles that disconnect AG from distant regions, and (iii) lesion extent into neighboring regions damaged by the same infarct. (2) To explain why similar symptoms can also be observed after damage to other regions, AG damage needs to be studied in terms of the networks of regions that AG functions with, and other independent networks that might subsume the same functions. (3) To explain inter-patient variability on AG symptomatology, the degree of recovery-related brain reorganisation needs to account for time post-stroke, demographics, therapy input, and pre-stroke differences in functional anatomy. (4) A better integration of the results from lesion and functional neuroimaging investigations of AG function is required, with only the latter so far considering AG function in terms of a hub within the default mode network. Overall, this review discusses why it is so difficult to fully characterize the AG syndrome from lesion data, and how this might be addressed with modern lesion-symptom mapping.

Responsible development of clinical speech AI: Bridging the gap between clinical research and technology

This perspective article explores the challenges and potential of using speech as a biomarker in clinical settings, particularly when constrained by the small clinical datasets typically available in such contexts. We contend that by integrating insights from speech science and clinical research, we can reduce sample complexity in clinical speech AI models with the potential to decrease timelines to translation. Most existing models are based on high-dimensional feature representations trained with limited sample sizes and often do not leverage insights from speech science and clinical research. This approach can lead to overfitting, where the models perform exceptionally well on training data but fail to generalize to new, unseen data. Additionally, without incorporating theoretical knowledge, these models may lack interpretability and robustness, making them challenging to troubleshoot or improve post-deployment. We propose a framework for organizing health conditions based on their impact on speech and promote the use of speech analytics in diverse clinical contexts beyond cross-sectional classification. For high-stakes clinical use cases, we advocate for a focus on explainable and individually-validated measures and stress the importance of rigorous validation frameworks and ethical considerations for responsible deployment. Bridging the gap between AI research and clinical speech research presents new opportunities for more efficient translation of speech-based AI tools and advancement of scientific discoveries in this interdisciplinary space, particularly if limited to small or retrospective datasets.

Stable multivariate lesion symptom mapping

Multivariate lesion-symptom mapping (MLSM) considers lesion information across the entire brain to predict impairments. The strength of this approach is also its weakness-considering many brain features together synergistically can uncover complex brain-behavior relationships but exposes a high-dimensional feature space that a model is expected to learn. Successfully distinguishing between features in this landscape can be difficult for models, particularly in the presence of irrelevant or redundant features. Here, we propose stable multivariate lesion-symptom mapping (sMLSM), which integrates the identification of reliable features with stability selection into conventional MLSM and describe our open-source MATLAB implementation. Usage is showcased with our publicly available dataset of chronic stroke survivors (N=167) and further validated in our independent public acute stroke dataset (N = 1106). We demonstrate that sMLSM eliminates inconsistent features highlighted by MLSM, reduces variation in feature weights, enables the model to learn more complex patterns of brain damage, and improves model accuracy for predicting aphasia severity in a way that tends to be robust regarding the choice of parameters for identifying reliable features. Critically, sMLSM more consistently outperforms predictions based on lesion size alone. This advantage is evident starting at modest sample sizes (N>75). Spatial distribution of feature importance is different in sMLSM, which highlights the features identified by univariate lesion symptom mapping while also implicating select regions emphasized by MLSM. Beyond improved prediction accuracy, sMLSM can offer deeper insight into reliable biomarkers of impairment, informing our understanding of neurobiology.

Insights gained over 60 years on factors shaping post-stroke aphasia recovery: A commentary on Vignolo (1964)

Aphasia is an acquired language disorder resulting from brain injury, including strokes which is the most common etiology, neurodegenerative diseases, tumors, traumatic brain injury, and resective surgery. Aphasia affects a significant portion of stroke survivors, with approximately one third experiencing its debilitating effects in the long term. Despite its challenges, there is growing evidence that recovery from aphasia is possible, even in the chronic phase of stroke. Sixty years ago, Vignolo (1964) outlined the primary challenges confronted by researchers in this field. These challenges encompassed the absence of an objective evaluation of language difficulties, the scarcity of evidence regarding spontaneous aphasia recovery, and the presence of numerous variables that could potentially influence the process of aphasia recovery. In this paper, we discuss the remarkable progress that has been made in the assessment of language and communication in aphasia as well as in understanding the factors influencing post-stroke aphasia recovery.

Neuromodulatory effects of transcranial magnetic stimulation on language performance in healthy participants: Systematic review and meta-analysis

Background

The causal relationships between neural substrates and human language have been investigated by transcranial magnetic stimulation (TMS). However, the robustness of TMS neuromodulatory effects is still largely unspecified. This study aims to systematically examine the efficacy of TMS on healthy participants' language performance.

Methods

For this meta-analysis, we searched PubMed, Web of Science, PsycINFO, Scopus, and Google Scholar from database inception until October 15, 2022 for eligible TMS studies on language comprehension and production in healthy adults published in English. The quality of the included studies was assessed with the Cochrane risk of bias tool. Potential publication biases were assessed by funnel plots and the Egger Test. We conducted overall as well as moderator meta-analyses. Effect sizes were estimated using Hedges'g (g) and entered into a three-level random effects model.

Results

Thirty-seven studies (797 participants) with 77 effect sizes were included. The three-level random effects model revealed significant overall TMS effects on language performance in healthy participants (RT: g = 0.16, 95% CI: 0.04-0.29; ACC: g = 0.14, 95% CI: 0.04-0.24). Further moderator analyses indicated that (a) for language tasks, TMS induced significant neuromodulatory effects on semantic and phonological tasks, but didn't show significance for syntactic tasks; (b) for cortical targets, TMS effects were not significant in left frontal, temporal or parietal regions, but were marginally significant in the inferior frontal gyrus in a finer-scale analysis; (c) for stimulation parameters, stimulation sites extracted from previous studies, rTMS, and intensities calibrated to the individual resting motor threshold are more prone to induce robust TMS effects. As for stimulation frequencies and timing, both high and low frequencies, online and offline stimulation elicited significant effects; (d) for experimental designs, studies adopting sham TMS or no TMS as the control condition and within-subject design obtained more significant effects.

Discussion

Overall, the results show that TMS may robustly modulate healthy adults' language performance and scrutinize the brain-and-language relation in a profound fashion. However, due to limited sample size and constraints in the current meta-analysis approach, analyses at a more comprehensive level were not conducted and results need to be confirmed by future studies.

Systematic review registration

[https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=366481], identifier [CRD42022366481].

Scanning agro-industrial wastes as substrates for fungal biopesticide production: Use of Beauveria bassiana and Trichoderma harzianum in solid-state fermentation

As a waste valorisation option, agro-industrial residues (rice husk, apple pomace, whisky draff, soy fiber, rice fiber, wheat straw, beer draff, orange peel and potato peel) were tested as feasible substrates for fungal conidia production. Solid-state fermentation tests were conducted at laboratory scale (100 g) with Beauveria bassiana or Trichoderma harzianum which conidia are reported to have biopesticide properties. Conidia concentrations with all substrates were at least two orders of magnitude above inoculum except for both fibers, thus demonstrating the possibilities of the proposed waste recovery option. Highest productions were at least 1 × 10⁹ conidia g^-1 dry matter for Beauveria bassiana using rice husk or potato peel and higher than 5 × 10⁹ conidia g^-1 dry matter for Trichoderma harzianum using beer draff, potato peel or orange pomace. Principal component analysis has been used to understand which parameters affect the most fungal conidia production for an easier evaluation of other similar wastes, being air-filled porosity and initial pH for Beauveria bassiana and cumulative oxygen consumption, initial moisture and total sugar content for Trichoderma harzianum.

Enhancing the Classification of Aphasia: A Statistical Analysis Using Connected Speech

BACKGROUND

Large shared databases and automated language analyses allow for the application of new data analysis techniques that can shed new light on the connected speech of people with aphasia (PWA).

AIMS

To identify coherent clusters of PWA based on language output using unsupervised statistical algorithms and to identify features that are most strongly associated with those clusters.

METHODS & PROCEDURES

Clustering and classification methods were applied to language production data from 168 PWA. Language samples were from a standard discourse protocol tapping four genres: free speech personal narratives, picture descriptions, Cinderella storytelling, procedural discourse.

OUTCOMES & RESULTS

Seven distinct clusters of PWA were identified by the K-means algorithm. Using the random forests algorithm, a classification tree was proposed and validated, showing 91% agreement with the cluster assignments. This representative tree used only two variables to divide the data into distinct groups: total words from free speech tasks and total closed class words from the Cinderella storytelling task.

CONCLUSION

Connected speech data can be used to distinguish PWA into coherent groups, providing insight into traditional aphasia classifications, factors that may guide discourse research and clinical work.

Machine learning-based multimodal prediction of language outcomes in chronic aphasia

Recent studies have combined multiple neuroimaging modalities to gain further understanding of the neurobiological substrates of aphasia. Following this line of work, the current study uses machine learning approaches to predict aphasia severity and specific language measures based on a multimodal neuroimaging dataset. A total of 116 individuals with chronic left-hemisphere stroke were included in the study. Neuroimaging data included task-based functional magnetic resonance imaging (fMRI), diffusion-based fractional anisotropy (FA)-values, cerebral blood flow (CBF), and lesion-load data. The Western Aphasia Battery was used to measure aphasia severity and specific language functions. As a primary analysis, we constructed support vector regression (SVR) models predicting language measures based on (i) each neuroimaging modality separately, (ii) lesion volume alone, and (iii) a combination of all modalities. Prediction accuracy across models was subsequently statistically compared. Prediction accuracy across modalities and language measures varied substantially (predicted vs. empirical correlation range: r = .00-.67). The multimodal prediction model yielded the most accurate prediction in all cases (r = .53-.67). Statistical superiority in favor of the multimodal model was achieved in 28/30 model comparisons (p-value range: <.001-.046). Our results indicate that different neuroimaging modalities carry complementary information that can be integrated to more accurately depict how brain damage and remaining functionality of intact brain tissue translate into language function in aphasia.

Common and distinct neural substrates of sentence production and comprehension

Functional neuroimaging and lesion-symptom mapping investigations implicate a left frontal-temporal-parietal network for sentence processing. The majority of studies have focused on sentence comprehension, with fewer in the domain of sentence production, which have not fully elucidated overlapping and/or unique brain structures associated with the two domains, particularly for sentences with noncanonical word order. Using voxel-based lesion symptom mapping (VLSM) we examined the relationship between lesions within the left hemisphere language network and both sentence comprehension and production of simple and complex syntactic structures in 76 participants with chronic stroke-induced aphasia. Results revealed shared regions across domains in the anterior and posterior superior temporal gyri (aSTG, pSTG), and the temporal pole (adjusted for verb production/comprehension). Additionally, comprehension was associated with lesions in the anterior and posterior middle temporal gyri (aMTG, pMTG), the MTG temporooccipital regions, SMG/AG, central and parietal operculum, and the insula. Subsequent VLSM analyses (production versus comprehension) revealed critical regions associated with each domain: anterior temporal lesions were associated with production; posterior temporo-parietal lesions were associated with comprehension, implicating important roles for regions within the ventral and dorsal stream processing routes, respectively. Processing of syntactically complex, noncanonical (adjusted for canonical), sentences was associated with damage to the pSTG across domains, with additional damage to the pMTG and IPL associated with impaired sentence comprehension, suggesting that the pSTG is crucial for computing noncanonical sentences across domains and that the pMTG, and IPL are necessary for re-analysis of thematic roles as required for resolution of long-distance dependencies. These findings converge with previous studies and extend our knowledge of the neural mechanisms of sentence comprehension to production, highlighting critical regions associated with both domains, and further address the mechanism engaged for syntactic computation, controlled for the contribution of verb processing.

Factor Analysis of Spontaneous Speech in Aphasia

Purpose Spontaneous speech tasks are critically important for characterizing spoken language production deficits in aphasia and for assessing the impact of therapy. The utility of such tasks arises from the complex interaction of linguistic demands (word retrieval, sentence formulation, articulation). However, this complexity also makes spontaneous speech hugely variable and difficult to assess. The current study aimed to simplify the problem by identifying latent factors underlying performance in spontaneous speech in aphasia. The ecological validity of the factors was examined by examining how well the factor structures corresponded to traditionally defined aphasia subtypes. Method A factor analysis was conducted on 17 microlinguistic measures of narratives from 274 individuals with aphasia in AphasiaBank. The resulting factor scores were compared across aphasia subtypes. Supervised (linear discriminant analysis) and unsupervised (latent profile analysis) classification techniques were then conducted on the factor scores and the solutions compared to traditional aphasia subtypes. Results Six factors were identified. Two reflected aspects of fluency, one at the phrase level (Phrase Building) and one at the narrative level (Narrative Productivity). Two other factors reflected the accuracy of productions, one at the word level (Semantic Anomaly) and one at the utterance level (Grammatical Error). The other two factors reflected the complexity of sentence structures (Grammatical Complexity) and the use of repair behaviors (Repair), respectively. Linear discriminant analyses showed that only about two thirds of speakers were classified correctly and that misclassifications were similar to disagreements between clinical diagnoses. The most accurately diagnosed syndromes were the largest groups-Broca's and anomic aphasia. The latent profile analysis also generated profiles similar to Broca's and anomic aphasia but separated some subtypes according to severity. Conclusions The factor solution and the classification analyses reflected broad patterns of spontaneous speech performance in a large and representative sample of individuals with aphasia. However, such data-driven approaches present a simplified picture of aphasia patterns, much as traditional syndrome categories do. To ensure ecological validity, a hybrid approach is recommended, balancing population-level analyses with examination of performance at the level of theoretically specified subgroups or individuals. Supplemental Material https://doi.org/10.23641/asha.13232354.