The Musical Structure of Time in the Brain: Repetition, Rhythm, and Harmony in fMRI During Rest and Passive Movie Viewing.

Space generally overshadows time in the construction of theories in cognitive neuroscience. In this paper, we pivot from the spatial axes to the temporal, analyzing fMRI image series to reveal structures in time rather than space. To determine affinities among global brain patterns at different times, core concepts in network analysis (derived from graph theory) were applied temporally, as relations among brain images at every time point during an fMRI scanning epoch. To explore the temporal structures observed through this adaptation of network analysis, data from 180 subjects in the Human Connectome Project were examined, during two experimental conditions: passive movie viewing and rest. The temporal brain, like the spatial brain, exhibits a modular structure, where "modules" are intermittent (distributed in time). These temporal entities are here referred to as themes. Short sequences of themes - motifs - were studied in sequences from 4 to 11 s in length. Many motifs repeated at constant intervals, and are therefore rhythmic; rhythms, converted to frequencies, were often harmonic. We speculate that the structure and interaction of these global oscillations underwrites the capacity to experience and navigate a world which is both recognizably stable and noticeably changing at every moment - a temporal world. In its temporal structure, this brain-constituted world resembles music.

Neural correlates of temporality: default mode variability and temporal awareness.

The continual background awareness of duration is an essential structure of consciousness, conferring temporal extension to the many objects of awareness within the evanescent sensory present. Seeking the possible neural correlates of ubiquitous temporal awareness, this article reexamines fMRI data from off-task "default mode" (DM) periods in 25 healthy subjects studied by Grady et al. ("Age-related Changes in Brain Activity across the Adult Lifespan,"Journal of Cognitive Neuroscience 18(2), 2005). "Brain reading" using support vector machines detected information specifying elapsed time, and further analysis specified distributed networks encoding implicit time. These networks fluctuate; none are continuously active during DM. However, the aggregate regions of greatest variability closely resemble the default mode network. It appears that the default mode network has an important role as a state-dependent monitor of temporality.

Mind as music.

Cognitive neuroscience typically develops hypotheses to explain phenomena that are localized in space and time. Specific regions of the brain execute characteristic functions, whose causes and effects are prompt; determining these functions in spatial and temporal isolation is generally regarded as the first step toward understanding the coherent operation of the whole brain over time. In other words, if the task of cognitive neuroscience is to interpret the neural code, then the first step has been semantic, searching for the meanings (functions) of localized elements, prior to exploring neural syntax, the mutual constraints among elements synchronically and diachronically. While neuroscience has made great strides in discovering the functions of regions of the brain, less is known about the dynamic patterns of brain activity over time, in particular, whether regions activate in sequences that could be characterized syntactically. Researchers generally assume that neural semantics is a precondition for determining neural syntax. Furthermore, it is often assumed that the syntax of the brain is too complex for our present technology and understanding. A corollary of this view holds that functional MRI (fMRI) lacks the spatial and temporal resolution needed to identify the dynamic syntax of neural computation. This paper examines these assumptions with a novel analysis of fMRI image series, resting on the conjecture that any computational code will exhibit aggregate features that can be detected even if the meaning of the code is unknown. Specifically, computational codes will be sparse or dense in different degrees. A sparse code is one that uses only a few of the many possible patterns of activity (in the brain) or symbols (in a human-made code). Considering sparseness at different scales and as measured by different techniques, this approach clearly distinguishes two conventional coding systems, namely, language and music. Based on an analysis of 99 subjects in three different fMRI protocols, in comparison with 194 musical examples and 700 language passages, it is observed that fMRI activity is more similar to music than it is to language, as measured over single symbols, as well as symbol combinations in pairs and triples. Tools from cognitive musicology may therefore be useful in characterizing the brain as a dynamical system.

Aberrant "default mode" functional connectivity in schizophrenia.

OBJECTIVE: The "default mode" has been defined as a baseline condition of brain function and is of interest because its component brain regions are believed to be abnormal in schizophrenia. It was hypothesized that the default mode network would show abnormal activation and connectivity in patients with schizophrenia. METHOD: Patients with schizophrenia (N=21) and healthy comparison subjects (N=22) performed an auditory oddball task during functional magnetic resonance imaging (fMRI). Independent component analysis was used to identify the default mode component. Differences in the spatial and temporal aspects of the default mode network were examined in patients versus comparison subjects. RESULTS: Healthy comparison subjects and patients had significant spatial differences in the default mode network, most notably in the frontal, anterior cingulate, and parahippocampal gyri. In addition, activity in patients in the medial frontal, temporal, and cingulate gyri correlated with severity of positive symptoms. The patients also showed significantly higher frequency fluctuations in the temporal evolution of the default mode. CONCLUSIONS: Schizophrenia is associated with altered temporal frequency and spatial location of the default mode network. The authors hypothesized that this network may be under- or overmodulated by key regions, including the anterior and posterior cingulate cortex. In addition, the altered temporal fluctuations in patients may result from a change in the connectivity of these regions with other brain networks.

Functional MRI and the study of human consciousness.

Functional brain imaging offers new opportunities for the study of that most pervasive of cognitive conditions, human consciousness. Since consciousness is attendant to so much of human cognitive life, its study requires secondary analysis of multiple experimental datasets. Here, four preprocessed datasets from the National fMRI Data Center are considered: Hazeltine et al., Neural activation during response competition; Ishai et al., The representation of objects in the human occipital and temporal cortex; Mechelli et al., The effects of presentation rate during word and pseudoword reading; and Postle et al., Activity in human frontal cortex associated with spatial working memory and saccadic behavior. The study of consciousness also draws from multiple disciplines. In this article, the philosophical subdiscipline of phenomenology provides initial characterization of phenomenal structures conceptually necessary for an analysis of consciousness. These structures include phenomenal intentionality, phenomenal superposition, and experienced temporality. The empirical predictions arising from these structures require new interpretive methods for their confirmation. These methods begin with single-subject (preprocessed) scan series, and consider the patterns of all voxels as potential multivariate encodings of phenomenal information. Twenty-seven subjects from the four studies were analyzed with multivariate methods, revealing analogues of phenomenal structures, particularly the structures of temporality. In a second interpretive approach, artificial neural networks were used to detect a more explicit prediction from phenomenology, namely, that present experience contains and is inflected by past states of awareness and anticipated events. In all of 21 subjects in this analysis, nets were successfully trained to extract aspects of relative past and future brain states, in comparison with statistically similar controls. This exploratory study thus concludes that the proposed methods for "neurophenomenology" warrant further application, including the exploration of individual differences, multivariate differences between cognitive task conditions, and exploration of specific brain regions possibly contributing to the observations. All of these attractive questions, however, must be reserved for future research.