The past two decades have seen significant advances in computational neuroscience . Functional neuroimaging is one of the prevalent advanced technologies that are used in most brain studies. Developing methods for analyzing neural responses involves many modalities of measurement in various species. Some of these modalities are single-unit recording, electrocorticography (ECoG), electro– and magnetoencephalography (EEG and MEG), and functional magnetic resonance imaging (fMRI). Most human brain studies employed fMRI data — a non-invasive brain imaging technique with unprecedented spatiotemporal resolution .
fMRI is an imaging technology that uses an indirect measure of oxygen levels in the brain, referred to as Blood Oxygen Level Dependent (BOLD) signals, to estimate the neural activities associated with brain cells . fMRI is a 4D data — it is a time series of 3D brain snapshots. Each 3D snapshot includes a set of voxels (3D pixels) that show the level of Oxygen usage in corresponding loci. This spatiotemporal resolution of fMRI data needs to be analyzed by using advanced machine learning techniques.
In general, fMRI can be captured in two different ways — viz., rest-state, and task-based. In a rest-state fMRI (rs-fMRI), subjects do not do any cognitive task during the imaging procedure. rs-fMRI can be used to compare brain networks between subjects with mental disease and control ones — such as rest network, default network, etc. rs-fMRI has several applications for prognosis and diagnosis of mental diseases — such as Alzheimer’s disease, Autism, etc.
Figure 1. An example of task-based fMRI data, where subject watches visual stimuli and the neural responses are showed in temporal cortex.
As Figure 1 depicted, task-based fMRI enables us to study the human brain when subjects are pursuing tasks. Here, task refers to the cognitive task performed by the subject during an fMRI experiment — e.g., watching photos, making decisions, etc. .
Figure 2. An example of a classification model for fMRI analysis. We learn a model by using training data (with labels) to predict testing data (without labels) .
Task-based fMRI can be analyzed with two different perspectives — viz, predictive methods, and similarity analysis . As Figure 2 shows, predictive approaches such as Multivariable Pattern (MVP) Classification learns a model by using a training-set — including, a subset of neural activities and their corresponding responses (aka, labels). This trained-model enables us to predict the labels for testing data — i.e., the new neural responses that are unseen during the learning procedure [1—3]. As an example, consider a visual task, where we are using an fMRI machine to capture the neural activities of five subjects as they are watching photos (stimuli). Here, this experiment is considering three categories of visual stimuli — e.g., watching photos of human faces, photos of chairs, photos of shoes. Note that all subjects are watching the same groups of visual stimuli. However, each stimulus — such as the specific face seen (perhaps Mr. Smith), or the particular shoe (maybe the black one) — can be different across subjects. A classification model can use the neural responses belonging to Subjects 1—4 to learn a model and then use this model and the neural responses of Subject 5 to determine when Subject 5 watched each of those visual stimuli — i.e., faces, shoes, and chairs [1, 3].
MVP classification techniques have a black-box approach for analyzing the neural responses. They cannot show which sub-regions are significantly involved with an accurate prediction. For instance, a classification can be used to predict Alzheimer’s disease based on the neural activities, but it cannot explain how much the cognitive processes are similar (or different) between groups of patients and controls, or even which brain regions generate distinguish neural signatures across different cognitive states [1, 4].
Figure 3. An example of fMRI similarity analysis between human brains and monkey brains for a common set of stimuli .
Similarity analysis is one of the popular techniques that can be used to explain similarities (or differences) between different cognitive states [4, 5]. This process first applies a similarity analysis method — such as Representational Similarity Analysis (RSA) — to generate a unique neural signature for each category of stimuli — e.g., one signature for any face watched by any subject, and another signature for any photo of shoes watched by any subject, etc. Neural signature refers to a vector that identifies which parts of the brain are involved with a category of stimuli. The generated neural signatures can be compared by using different similarity metrics — such as correlation — for understanding different cognitive states or demonstrating the neurological process both within and
between brain regions.
In summary, fMRI enables us to ask what information is represented in a region of the human brain and how that information is encoded, instead of asking what is the function of a region [1, 6]. It can be used to find novel treatments for mental diseases or even to create a new generation of the user interface [1, 6].
- Decoding Neural Representational Spaces Using Multivariate Pattern Analysis. DOI: https://doi.org/10.1146/annurev-neuro-062012-170325
- Supervised Hyperalignment for multi-subject fMRI data alignment. DOI: 10.1109/TCDS.2020.2965981.
- Multi-Objective Cognitive Model: a supervised approach for multi-subject fMRI analysis. DOI: 10.1007/s12021-018-9394-9
- A Bayesian method for reducing bias in neural representational similarity analysis. Link: https://papers.nips.cc/paper/6131-a-bayesian-method-for-reducing-bias-in-neural-representational-similarity-analysis.pdf
- Matching categorical object representations in inferior temporal cortex of man and monkey. DOI: https://dx.doi.org/10.1016%2Fj.neuron.2008.10.043
- Local Discriminant Hyperalignment for multi-subject fMRI data alignment. Link: https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/download/14347/13743