Published in 1909, Korbinian Brodmann's groundbreaking analysis of the brain can still be found in neurology textbooks and on classroom posters to this day. Using a specialized microscope, Brodmann painstakingly analyzed the entire surface of the Cerebral Cortex on cellular structure alone. After a decade of effort, Brodmann produced the most detailed map of the Cerebral Cortex yet produced, assigning each region a different number. Over time these areas have been widely used to link brain regions with specific functions, such as area four: the primary motor cortex. This region of the Cerebral Cortex is believed to control motor movements such as moving the hands and face as well as breathing and voluntary blinking. Brodmann's areas have also been mapped to functions such as processing numbers, planning, and processing emotions. Of course, the complexity doesn't stop there as scientists now believe the Cortex has at least 180 distinct regions important for language, perception, consciousness, and attention.

Aphasia, a language disorder resulting from brain damage, requires accurate identification of specific aphasia types, such as Broca's and Wernicke's aphasia, for effective treatment. However, little attention has been paid to developing methods to detect different types of aphasia. Recognizing the importance of analyzing co-speech gestures for distinguish aphasia types, we propose a multimodal graph neural network for aphasia type detection using speech and corresponding gesture patterns. By learning the correlation between the speech and gesture modalities for each aphasia type, our model can generate textual representations sensitive to gesture information, leading to accurate aphasia type detection. Extensive experiments demonstrate the superiority of our approach over existing methods, achieving state-of-the-art results (F1 84.2\%). We also show that gesture features outperform acoustic features, highlighting the significance of gesture expression in detecting aphasia types. We provide the codes for reproducibility purposes.

This paper presents a fully automated approach for identifying speech anomalies from voice recordings to aid in the assessment of speech impairments. By combining Connectionist Temporal Classification (CTC) and encoder-decoder-based automatic speech recognition models, we generate rich acoustic and clean transcripts. We then apply several natural language processing methods to extract features from these transcripts to produce prototypes of healthy speech. Basic distance measures from these prototypes serve as input features for standard machine learning classifiers, yielding human-level accuracy for the distinction between recordings of people with aphasia and a healthy control group. Furthermore, the most frequently occurring aphasia types can be distinguished with 90% accuracy. The pipeline is directly applicable to other diseases and languages, showing promise for robustly extracting diagnostic speech biomarkers.

Damage to the inferior frontal gyrus (Broca's area) can cause agrammatic aphasia wherein patients, although able to comprehend, lack the ability to form complete sentences. This inability leads to communication gaps which cause difficulties in their daily lives. The usage of assistive devices can help in mitigating these issues and enable the patients to communicate effectively. However, due to lack of large scale studies of linguistic deficits in aphasia, research on such assistive technology is relatively limited. In this work, we present two contributions that aim to re-initiate research and development in this field. Firstly, we propose a model that uses linguistic features from small scale studies on aphasia patients and generates large scale datasets of synthetic aphasic utterances from grammatically correct datasets. We show that the mean length of utterance, the noun/verb ratio, and the simple/complex sentence ratio of our synthetic datasets correspond to the reported features of aphasic speech. Further, we demonstrate how the synthetic datasets may be utilized to develop assistive devices for aphasia patients. The pre-trained T5 transformer is fine-tuned using the generated dataset to suggest 5 corrected sentences given an aphasic utterance as input. We evaluate the efficacy of the T5 model using the BLEU and cosine semantic similarity scores. Affirming results with BLEU score of 0.827/1.00 and semantic similarity of 0.904/1.00 were obtained. These results provide a strong foundation for the concept that a synthetic dataset based on small scale studies on aphasia can be used to develop effective assistive technology.

Using language makes human beings surpass animals in wisdom. To let machines understand, learn, and use language flexibly, we propose a human-like general language processing (HGLP) architecture, which contains sensorimotor, association, and cognitive systems. The HGLP network learns from easy to hard like a child, understands word meaning by coactivating multimodal neurons, comprehends and generates sentences by real-time constructing a virtual world model, and can express the whole thinking process verbally. HGLP rapidly learned 10+ different tasks including object recognition, sentence comprehension, imagination, attention control, query, inference, motion judgement, mixed arithmetic operation, digit tracing and writing, and human-like iterative thinking process guided by language. Language in the HGLP framework is not matching nor correlation statistics, but a script that can describe and control the imagination.

A century and a half ago, French physician Pierre Paul Broca found that patients with damage to part of the brain's frontal lobe were unable to speak more than a few words. Later dubbed Broca's area, this region is believed to be critical for speech production and some aspects of language comprehension. However, in recent years neuroscientists have observed activity in Broca's area when people perform cognitive tasks that have nothing to do with language, such as solving math problems or holding information in working memory. Those findings have stimulated debate over whether Broca's area is specific to language or plays a more general role in cognition. A new study from MIT may help resolve this longstanding question.

The brain looks like a featureless expanse of folds and bulges, but it's actually carved up into invisible territories. Each is specialized: Some groups of neurons become active when we recognize faces, others when we read, others when we raise our hands. On Wednesday, in what many experts are calling a milestone in neuroscience, researchers published a spectacular new map of the brain, detailing nearly 100 previously unknown regions -- an unprecedented glimpse into the machinery of the human mind. Scientists will rely on this guide as they attempt to understand virtually every aspect of the brain, from how it develops in children and ages over decades, to how it can be corrupted by diseases like Alzheimer's and schizophrenia. "It's a step towards understanding why we're we," said David Kleinfeld, a neuroscientist at the University of California, San Diego, who was not involved in the research.

In fact, goal-driven hierarchical structure to concatenate simple human behavior is mostly constituted by goal-directed motor acts. This hierarchical goal structure as well as the actions based on the synergic composition of simpler rules, which connect individual motor elements, might be motor constituents chained together according to a precise paralleled to the syntactic organization of language.