A Beginner’s Guide to Brain Analysis

As a new Ph.D. student delving into brain analysis, I realized there are many diverse challenges and concepts (e.g., data success, data privacy, data conversion, variety of brain scans, etc.) that are required for analyzing the brain. My life would have been easier if I had one document to outline the concepts/mechanisms I needed to explore. As such, I am writing this article to summarize my journey, in the hopes that it is going to help the next person trying to immerse themselves in brain analysis.

Thus, the purpose of this article is to provide readers with a general understanding of how to get started with brain data analysis and the associated challenges.

First, a brief explanation of my motivation. I am interested in using computer technology to improve healthcare services, and in developing machine learning models to make intelligent decisions. I’m particularly interested in brain analysis because neuroscientists can peek inside the living brain using brain imaging techniques. These techniques aid neuroscientists in understanding the connections between different parts of the brain and the functions they perform. Brain images provide you a thorough picture of your brain. They can assist doctors in detecting and diagnosing problems including tumors, stroke causes, and vascular dementia also, scientists can compare the function and architecture of neurons in healthy persons to neurons in diseased people’s brains. Scientists can investigate individual cells in neurons to aid in the treatment of brain illnesses. Imaging devices are used by scientists to gain a better understanding of the working brain. positron emission tomography (PET), Magnetic resonance imaging (MRI), computerized tomography (CT), and Functional Magnetic Resonance Imaging are the techniques often used to investigate the brain.

The study of the neural connections and the processing of information that enters and exits the human brain has benefited greatly from computer science. It is feasible to convert biological instructions released by neurons via action potentials into binary codes, which are digital orders that machines can understand, using algorithms. As a result, computer science combined with neurology provides a slew of advancements to the medical profession, allowing for the development of technologies that can improve and transform people’s quality of life.

Step 1: Understanding the biological structure of the brain

The first step in understanding what this article is about is to understand what the brain is, what it does, and what each portion of the brain does. The cerebrum, cerebellum, and brainstem are the three primary regions of the brain. The cerebrum, which is made up of the right and left hemispheres, is the largest region of the brain.

Step 2: Tools for obtaining Brain Data

The second step, after understanding what the brain is, is for this article to go over in-depth the various technologies used to collect brain data. Cognitive neuroscientists can use positron emission tomography (PET), Magnetic resonance imaging (MRI), computerized tomography (CT), and functional magnetic resonance imaging (fMRI) to image the brain.

Step 3: Obtaining Brain Dataset

The final step of this article explains how to get a hold of a brain dataset for analysis, which can be accomplished in one of two methods: direct data collection through a medical facility or using publicly available data.

The Brain’s Anatomy

The field of neuroscience is quickly expanding our understanding of the brain, nervous system, and ourselves. But what is the brain? The brain is a complex organ that controls all the actions in the body, which includes emotion, thought, memory, touch, vision, and respiration. The brain and the spinal cord are made up of the spinal cord that extends from it which makes it the most important part of the human body. The brain consists of around 100 billion nerves that interact in billions of synapses to communicate, which makes it one of the biggest and most complicated organs in the body. The brain works in a similar way to an expert council. Even though all the brain’s parts work together, each one has its own distinct qualities. The three basic units that make up the brain are the forebrain, midbrain, and hindbrain.

The brain anatomy is a large topic to discuss in this article, but the overview will provide you with an outline of what you need to know. To elucidate further, the brain is divided into various specialized parts that work altogether. The brain’s outermost layer is the cortex and these controls thinking and voluntary activities, and between the spinal cord and the rest of the brain is where the brain stem is located, also the basic roles of breathing and sleeping are controlled by the spinal cord. Second, the basal ganglia are a collection of structures in the brain’s core that send and receive messages from across the brain. Finally, near the base and back of the brain is found the cerebellum and this oversees the balance and coordination, while the frontal lobes of the brain oversee problem-solving, judgment, and motor function, while the sensation, handwriting, and body movement are overseen by the parietal lobes. The temporal lobes handle hearing and memory, whereas the visual processing system is controlled by the occipital lobes. Here are some resources to help you understand the brain better. Anatomy of the Brain, Brain Anatomy and How the Brain Works, Anatomy of the Brain, Brain Anatomy, All About The Brain: Anatomy, Conditions, and Keeping It Healthy

Tools for Obtaining Brain Data

Effective data collection offers the information required to answer questions, analyze corporate performance or other outcomes, and forecast future trends, actions, and scenarios in data analytics applications and research initiatives. This article is mostly concerned with brain data and to appreciate the intricacy of their information, brain data comprises simultaneous measurements on a variety of elements, as well as interactions between distinct brain data scans in a multivariate manner. This section will go over the steps for various brain scans in detail.

PET Scan

A PET (Positron Emission Tomography) scan is an imaging test that displays the functions of the tissues and organs. It employs radiotracers to monitor and assess metabolic changes. It comes with 3D pictures and is utilized in research to answer specific questions as well as to aid researchers in the study of normal and abnormal processes. The dye used in the pet scan contains radioactive tracers, which are used in imaging examinations to assist find problems inside the body. These radioactive tracers also emit particles that identify and converted into a picture to detect organ problems. A PET scan produces radiation of around 25 mSv (millisievert). The procedure includes a tracer that is injected into a vein, the absorption of the tracer is determined by the part of the body being scanned and this procedure requires being completely still. The PET scans result show metabolic differences which occurs at the cellular stage in an organ or a tissue, and PET scans can detect very early alterations in your cells and the brain imaging from a PET scan appears like a multicolored representation of the brain which ranges from dark blue to deep red. Warmer colors, such as yellow and red, indicate active brain activity, and the medic will examine the scans for anomalies.

Pros

1. PET scans are the greatest option for people who are afraid of contracting an infection because of medical operations.
2. PET scans are a little safer because the radiation dose is rather low.
3. PET scans are the most precise medical tools available for reducing the number of needless procedures performed because of incorrect staging data and diagnosis.

Cons

1. Even if the radioactive components utilized in the scans are short-lived, the patient is nevertheless exposed to radioactive rays, which is harmful and limits the number of times PET imaging can be performed.
2. Even though the radioactive elements employed in these scans have a limited half-life, they may create difficulties, particularly in pregnant individuals.

MRI Scan 

Magnetic resonance imaging (MRI scan): Magnetic resonance image scanner provides detailed images of the brain and other regions of the skull by using radio waves in a magnetic field. It can detect nearly every component of the body, including the brain, spinal cord, internal organs, bones, and so on. It’s utilized in research to answer specific queries and to help scientists understand more about the brain’s shape and function which comes in 2D and 3D formats. The magnetic resonance scan employs a solid radio wave and magnetic which provide images of the body that x-rays and CT scans can’t see. A particular material is known as a “contrast agent” is injected into the veins and transported to the brain during angiography (brain angiogram).

Pros

1. Radiation is not used in MRI.
2. Other imaging techniques can’t match MRI’s ability to produce exceptionally crisp, detailed images of soft-tissue structures.
3. MRI can produce hundreds of pictures in practically any direction and orientation.

Cons

1. Because MRI cannot consistently distinguish between malignant tumors and benign, false positive results may occur.
2. The strong magnet of the MRI unit may damage an undiscovered metal implant in a patient’s body.
3. There is a potential that a patient will have an adverse response to the contrasting agent or develop a skin infection at the injection site.

fMRI Scan

A functional magnetic resonance imaging (fMRI) scan measures and maps the brain’s activity. It is used to analyze the functional anatomy of the brain, as well as to assess the impact of disorders, and serve as a therapy guide for the brain. It is used in research to address specific questions. Multiple processes are used to rectify any kind of noise, motion, signal drifts, slice timing discrepancies, and spatial distortions in fMRI data, including acknowledgment of outlier data. It’s also available in 2D pictures. The scan employs the same technology as an MRI scan, but it produces an image of the brain’s blow flow. The process includes the injection of contrast dye, which allows the tissue and blood vessels to be seen in greater detail and if the patient is claustrophobic, a sedative is provided, but newborns are given anesthetics since they must remain motionless throughout the procedure to avoid hazy output and the result of the scan must be examined by a radiologist and this used to detect abnormalities within the brain that cannot be found with other imaging techniques.

Pros

1. Unlike other scanning techniques (like as PET), fMRI does not involve radiation and is hence completely safe.
2. Produces images with excellent spatial resolution, displaying millimeter-level detail.

Cons

1. A 5-second lag between initial brain activity and picture results in poor temporal resolution.
2. It’s possible that the data doesn’t accurately reflect real-time brain activity.

CT Scan

The computerized tomography scan combines a sequence of X-ray’s taken from several angles all through the body with computer processing which generates cross-sectional images (which is known as slices) of the bones, soft tissues, and blood vessels. A computed tomography scan can detect bone and joint disorders including fractures and cancer, as well as interior injuries like those received in a car accident. It comes in 2D graphics and is used to answer specific inquiries in the study. The scan is a diagnostic imaging exam that generates an image in the body using x-ray technology. The process includes a specific dye known as contrast meterialto which allows a clear picture of the internal structure of the body that can’t be clearly in x-ray imaging; this gap material blocks x-rays and looks white on the image.

Pros

1. The CT scan could take pictures of blood arteries, soft tissues, and bones all at the same time.
2. The requirement for patients to remain fully immobile during a CT scan is less stringent.
3. Almost all partial CT scans are completed in a matter of seconds. A full-body CT scan, on the other hand, might be completed in under 30 minutes.

Cons

1. Iodine is used in a CT scan to scan a patient. Iodine allergy affects several people.
2. CT scans are usually not recommended for pregnant women unless they are medically necessary.
3. One of the downsides of CT is that it emits a larger amount of radiation than traditional radiography.

PET scans employ a tracer which is radioactive to shows the performance of the organ in real-time, MRI scans use magnets, and fMRI scans are a cycle of MRIs that determine brain function using a computer’s compilation of many images recorded less than a second apart. X-rays are used in computed tomography (CT) examinations, as well as radio waves. The absorption data is used by a computer to display the levels of activity as a color-coded brain map, with one color (typically red) indicating more active brain areas and another color (blue) indicating less active brain parts.

Preprocessing Brain Dataset

For our project to study fluid intelligence, we used The National Institute of Mental Health Data Archive (NDA) which holds various research data repositories, and The Skull stripped, T1-weighted images, volumetric brain measure, and residual fluid intelligence are among the items in this repository. At the end of this article, links to other publicly available datasets are provided to enable readers to see the various types of brain data that are available for use based on their interests.

The data format can be limited based on the tool used to retrieve them; for example, MRI, fMRI, PET, and CT scans use a DICOM format as an output, but some formats for neuroimaging can be in NIFTI, which limits its use unless it is converted to a regular image like JPG or PNG image, and data scientists can’t control the format of a data because most of the data used are secondary, so they don’t have access when the data is taken.

Image files are easier to deal with because they provide all the necessary visualization for data analysis while also being simple to navigate and Image interpretation is simple.

This paragraph explains how to convert brain datasets that come in Dicom (.dcm) or NIFTI (.nii) to JPG or PNG using python or MATLAB, as well as a GitHub link on how to convert these brain images.

Python and MATLAB can be used to convert the MRI brain data to a jpg image (into slices), the MRI brain dataset comes in neuroimaging(.nii) or Digital images classified(.dcm). It converts all images in a folder to JPG/PNG and extracts all data in a ‘.csv’ format all at once using Python. JPG / PNG is the most widely accepted file system for image processing and image classification. It’s difficult to work with a ‘nil’ or ‘. DCM image. The python program to convert image data is broken down into three parts: converting to JPG/PNG and extracting all information at once, only converting to JPG/PNG and extracting the patient’s information in a CSV file, and only converting to JPG/PNG and extracting the patient’s information in a CSV file. Basically, the application operates in an easy manner, which includes importing the library, specifying the format to be converted to (PNG or JPG), specifying the folder to extract to, and listing accessible image data attributes. This is a GitHub repository with a python sample program. In MATLAB: Any data ending in.nii.gz cannot be read by MATLAB, thus we must convert it to a MATLAB-friendly format. After downloading the file, unzip it and convert a single slice with a tool called MRIcro. To convert many slices, split the volume into slices and convert a single slice, split the volume into slices, and convert a single slice.

Security and Privacy Issues When Handling Brain Dataset

Health care databases are a valuable resource for identifying and correcting health disparities in patient populations. For a variety of reasons, data privacy in healthcare is crucial. Maintaining the security and confidentiality of patients’ information fosters trust, which improves the healthcare system. Maintaining patient privacy also aids in the protection of data from malicious actors. A breach can and does occur, which is why it is critical to always keep health-related information secure.

To have access to a brain dataset, there are some steps required to be fulfilled in other to gain access to brain data. It depends on the organization that holds the dataset that grabs your interest, this article focuses on The National Institute of Mental Health Data Archive (NDA) which holds various research data repositories. The selection of this repository is mainly because of the Skull stripped, T1-weighted pictures, volumetric brain measure, and residual fluid intelligence, and this repository contain all of the data for my research on the brain analysis study of fluid intelligence The dataset is publicly accessible and the procedure is simple: create an account, then contact the helpdesk for account activation. , in addition, to having access to any given dataset, you must ensure your institute’s federal wide assurance is valid. Once your account is activated, you will fill out a Data permission request form that includes why you are interested in the data, what data do you plan to access and how do you want to use the data and then you fill out a research data use statement and it must be signed by a dedicated signing official from your institute, then your request is sent for data access committee for review. Once the request is approved you have to download the data by firstly downloading the NDA application and login with your username and password and then you load the data into a checkout list on the website and then you download it via the NDA application on your workstation, and note, due to privacy and security you can only use the data at the lab registered to the institute which is dedicated to you and supervisor if you have one.

Challenges of Getting Brain Dataset

The issues stated below are primarily for The National Institute of Mental Health Data Archive (NDA) dataset, however other datasets may have different privacy policies that aren’t listed here.

Privacy

This is the challenging part of accessing a Brain dataset is the privacy policy. To access The National Institute of Mental Health Data Archive (NDA) dataset, the data use certification is required to get permission to access the dataset which includes:

1. Non-transferability of Agreement: This DUC cannot be transferred. Changes in institutional affiliation must be reported to the NIMH Data Archive.
2. Data for Research Use: Recipients agree to use the data for scientific study, scholarship, teaching, or any other type of research and development.
3. No Distribution of Data: Recipients agree to keep custody of data and not disseminate, sell, or move data, in any form, to any other person, company, or third-party system, for any reason.
4. Collaboration with Shared Data: Authorized researchers (collaborators) who are listed on a non-expired DUC for the same Permission Group and have agreed to the terms in this DUC may disseminate (share) data from the NIMH Data Archive for the purpose of cooperating on research projects only.
5. No Re-identification of Subjects: Data will not be utilized to try to establish the individual identities of any of the study participants from whom data was obtained, recipients agree.
6. Compliance with Applicable Human Subjects Protection and Institutional Requirements: Recipients undertake to follow all applicable guidelines for the protection of human subjects, which may include the Department of Health and Human Services’ 45 C.F.R. Part 46 regulations, as well as other federal and state laws.
7. Security: Recipients agree to safeguard data from the NIMH Data Archive by putting in place the safeguards necessary to ensure the data’s confidentiality, integrity, and availability.
8. Deletion of Data: Recipients undertake to completely erase data downloaded from the NIMH Data Archive from all local or cloud-based machines once research is concluded or this DUC expires, whichever comes first.
9. Supporting Documentation: Qualified researchers may access data and supporting documentation in the NIMH Data Archive, according to the terms outlined in this DUC.
10. Sharing Results with a NIMH Data Archive Study: Recipients agree to create and share an NIMH Data Archive Study (https://nda.nih.gov/get/manuscriptpreparation.html) for each publication, computational pipeline, or other public disclosure of results from the analysis of data accessed in the NIMH Data Archive, whether reporting positive or negative results, thereby linking it to the underlying data, whether reporting positive or negative results.
11. Acknowledgements: In all oral and written presentations, disclosures, and publications (including abstracts, as space allows) resulting from any and all analyses of data, recipients agree to acknowledge the appropriate NIMH Data Archive data repository and the relevant Digital Object Identifier(s) (DOI), which will be minted upon NIMH Data Archive Study creation.
12. Data Disclaimers: The National Institutes of Health (NIH) does not and cannot guarantee the outcomes of any data or data analysis tools offered in the NIMH Data Archive.
13. Non-Governmental Endorsement; Liability: Recipients agree that the United States Government, the Department of Health & Human Services, the National Institutes of Health, or the National Institute of Mental Health do not claim, infer, or imply endorsement of the research project described in the Research Data Use Statement, the entity, or personnel conducting the research project, or any resulting commercial product(s) by the United States Government, the Department of Health & Human Services, the National Institutes of Health, or the National Institute of Mental Health.
14. Recipient’s Permission to Post Information Publicly: The Recipient agrees to allow the NIMH Data Archive to publish a summary of the Recipient’s data research, as well as the Recipient’s name and organizational/institutional affiliation, as specified in this DUC.
15. Privacy Act Notification: Recipients agree that the NIH may make public, in part or in whole, information obtained from them as part of the DUC for tracking and reporting reasons.
16. Amendments: Amendments to this DUC must be made in writing and signed by all parties’ authorized representatives.
17. Termination: Either party may terminate this DUC without cause if the other party receives 30 days’ written notice.
18. Term and Access Period: For a period of one year, recipients are permitted access to requested and authorized data from the NIMH Data Archive, after which this DUC will automatically expire.
19. Violations: If you break any of the terms and conditions of this DUC, you could lose access to NDA data and face additional consequences.
20. Accurate Representations: The recipients fully attest that all claims stated or reflected in this document are true and accurate.

These documents must be signed by all the recipients and Authorized Institutional Business officials.

Brain Dataset Features

The National Institute of Mental Health Data Archive (NDA)  contains the Skull stripped, training set, the validation, the test set, and the fluid intelligence of using T1-weighted magnetic resonance images from 4 to 139 months. There are two regions of the central nervous system which is gray matter and white matter. The gray matter simply implies the darker and outer part of the brain, whereas the white matter implies the lighter and interior parts. This system is inverted in the spinal cord, with the white matter on the outer part and gray matter on the inside part. The MRI brain data is a Voxel (Each of an array of volume elements with a three-dimensional space is referred to as a voxel.) and each image shape is (240, 240, 240).  A voxel is a 3D image’s smallest recognizable box-shaped component. It’s the 3D version of a two-dimensional pixel.  The NDA brain dataset contains  122 features which can be categorized into the grey matter volume, white matter volume, and cerebral spinal fluid volume, and these features are used to create various models like analysis of feature importance, fluid intelligence prediction, converting magnetic resonance image (MRI) to Volumetric brain measure and interesting models other using machine learning.

The ABCD Neurocognitive Prediction Challenge is a cooperation between The National Institute of Mental Health Data Archive (NDA) and The ABCD Neurocognitive Prediction Challenge, which invites researchers to demonstrate their method of predicting fluid intelligence from T1-weighted MRI (about 8.5K subjects in total, age 9-10 years). The ABCD project is the country’s greatest long-term investigation of brain development and child health and The National Institute of Mental Health Data Archive (NDA) makes these human data subjects available.

Existing Brain Datasets

There are multiple brain datasets accessible for research purposes, however, they vary in terms of study goal and brain data type. This section provides an overview of the various brain datasets available and what they entail.

1. The National Institute of Mental Health Data Archive (NDA):  The (NDA), which includes the RDoC Database, the National Database for Clinical Trials Related to Mental Illness (NDCT), and the NIH Pediatric MRI Repository, is a set of repositories (PedsMRI).
2. Alzheimer’s Disease Neuroimaging Initiative (ADNI): The (ADNI) is a multisite experiment aimed at improving clinical trials for Alzheimer’s disease prevention and therapy (AD). This collaborative study brings together business and public sector expertise and financing to examine participants with Alzheimer’s disease, as well as those who may develop the disease and healthy controls with no evidence of cognitive impairment.
3. NeuroVault: Researchers can use NeuroVault to save and share unthresholded statistical maps, parcellations, and atlases generated by magnetic resonance image and Positron Emission Tomography investigations with the public.
4. OpenNeuro: OpenNeuro is a free and open platform for BIDS-compliant MRI, PET, MEG, EEG, and iEEG data validation and sharing.
5. Brain-Development: This brain-development contains resources for computational brain development analysis.
6. Cambridge Centre for Ageing and Neuroscience (Cam-CAN): The Cam-CAN research is looking at epidemiological, behavioral, and neuroimaging data to see if people can keep their cognitive ability in their later years

Overview of work done on the NDA Dataset

The (NDA) dataset repository has been combined with a variety of machine learning algorithms to predict fluid intelligence in the Adolescent Brain Cognitive Development Neurocognitive Prediction.

Using Deep Learning to Predict Fluid Intelligence

A Combined Deep Learning-Gradient Boosting Machine Framework for Fluid Intelligence Prediction, A gradient boosting machine structure is paired with deep learning. By anticipating the 123 continuous-valued developed the data presented with each magnetic resonance image, to reduce the high-dimensional magnetic resonance image data by training a convolutional neural network to understand the important image attributes. The gradient boosting machine is used to train which predicts the fluid intelligence score.

Deep Learning vs. Classical Machine Learning: A Comparison of Methods for Fluid Intelligence Prediction, Traditional machine learning models are trained on extracted characteristics, while deep learning models are developed on raw imagery. To obtain the best-performing answer, a traditional machine learning model is trained on a combination of given brain volume estimations and extracted attributes.

Using Ensemble Method to Predict Fluid Intelligence

Ensemble of SVM, Random-Forest, and the BSWiMS Method to Predict and Describe Structural Associations with Fluid Intelligence Scores from T1-Weighed MRI, The predictions of three machine learning algorithms, Random Forest, Support Vector Machine, and Bootstrapped Step Wise Model Selection, are proposed to be combined. The data produced 122 volumetric ratings, which were used to train a gender stratified SVM on children based on their age (ages 9–10). Gender-stratified and trained RF and BSWiMS employing cubic root transformed data, shortened by left-right mean and relative pure differences, and supplemented with 19 volumetric statistical signifiers of anatomical regions.

Predict Fluid Intelligence of Adolescents Using Ensemble Learning, The proposed ensemble method, which uses volume data from T-1 weighted brain images, provides much better fluid intelligence prediction accuracy than a single prediction model. In addition, the data from the Human Connectome Project (HCP) to compare outcomes in adolescence and young adults. It can be seen that the raw fluid intelligence score of HCP is much better predicted by the structure of the brain without regression of covariates such as age and volume of the brain. Also, predictions are generally more accurate for young adults than for adolescents.

Predicting Fluid Intelligence in Adolescent Brain MRI Data: An Ensemble Approach, In this paper, the Caruana Ensemble Search method is suggested for picking the most effective models from a big and heterogeneous pool of applicant models. The convolutional neural networks are applied to brain regions thought to be important in fluid intelligence which is with the models of high-performing standard machine learning methods which is utilized to the region-based scores like volume, mean intensity, and count of gray matter voxels.

Ensemble Modeling of Neurocognitive Performance Using MRI-Derived Brain Structure Volumes, The machine learning problem is the cognitive performance from structural imaging data in the brain. Using various machine learning models to address this problem, the findings demonstrate that models based on boosted decision trees perform better and that employing two separate sets of derived brain volumetric data is beneficial.

Using Cortical Grey Matter and White Matter to Predict Fluid Intelligence

ABCD Neurocognitive Prediction Challenge 2019: Predicting Individual Residual Fluid Intelligence Scores from Cortical Grey Matter Morphology, T1-weighted MRI data were used to predict fluid intelligence utilizing structural comparison of grey matter regions around the cortex. Distinct basic covariance networks are extracted into graph-theory metrics which is an average of across nodes throughout the brain.

Adolescent Fluid Intelligence Prediction from Regional Brain Volumes and Cortical Curvatures Using BlockPC-XGBoost, Other metrics such as mean cortical curvature, white matter volume, and subcortical volume, as well as gray matter volume of cortical areas, are discovered to have additional capacities in the prediction of pre-residualized fluid intelligence scores for teenagers in the dataset. The MSE score on the validation set progressed from 70.65 to 69.3 and the R-square score on the validation set progressed from 0.0175 to 0.0350.

Cortical and Subcortical Contributions to Predicting Intelligence Using 3D ConvNets, Predicting the residualized fluid intelligence scores using 3D convolutional neural was introduced as a unique method. Numerous cortical and subcortical brain regions were acknowledged in the framework, where the errors predicted were lesser than arbitrary guessing in the validation set with the mean squared error of 71.5252 and the result of the mean squared error is 70.5787 in the validation set, and 92.7407 on the test.

Predicting Intelligence Based on Cortical WM/GM Contrast, Cortical Thickness, and Volumetry, For fluid intelligence scores prediction from T1-weighted MR images, a fully connected four-layer neural network was developed. The fully connected neural network uses cortical white matter/grey matter contrast and cortical thickness at 78 cortical areas in addition to the volumes of brain shapes. The cortical surfaces generated by the CIVET process were used to obtain the last two metrics from T1-weighted MR images. The learning algorithm also considers the subjects’ age and gender, as well as the scanner manufacturer. With two hidden layers of 20 and 15 nodes, a total of 283 features were provided to the FNN.

Other Methods used to Predict Fluid Intelligence

Predicting Fluid Intelligence from Structural MRI Using Random Forest regression, The features included three additional groupings of features, as well as the volumes of gray matter regions of interest and these include ROI-based signal intensity features as well as shape-based features generated from the corpus callosum’s anterior and posterior cross-sectional areas. For prediction, a random forest regressor model was employed.

Predicting Fluid Intelligence of Children Using T1-Weighted MR Images and a StackNet, T1-weighted magnetic resonance images were combined with StackNet to predict fluid intelligence in teenagers in this work. The framework comprises feature extraction, denoising, selection, normalization, training of StackNet, and fluid intelligence prediction. The feature recovered is the supply of various brain tissues in various brain parcellation zones. Three layers and 11 models make up the proposed StackNet. Each layer, including the input layer, uses the predictions from all preceding layers.

Predicting Fluid Intelligence from MRI Images with Encoder-Decoder Regularization, Fluid intelligence was predicted using a 3D convolutional neural network using encoder-decoder regularization from the T1-weighted MRI images. Knowing that the cerebellar volume is significantly associated with intelligence, it was proposed to incorporate this information into the structure of the design for fluid intelligence prediction using encoder-decoder regularization for segmentation of brain construct concurrently. To construct the brain segmentation mask, an encoder-decoder network is trained to learn the discriminative morphological characteristic of the brain volume. The network’s encoder path is then reprocessed as the backbone for the prediction network for the final fluid intelligence prediction and the addition of a regression segment to forecast the fluid intelligence value.

Conclusion

This article, in my opinion, will assist future Ph.D. students in learning the principles of brain analysis as well as giving insight into the measures to take which will provide a pathway forward in brain analysis. Now that we’ve studied brain structure, different types of brain scans, how to get them, and how they function, this article compares them. One thing to keep in mind with these concepts is that each piece of brain data is unique. There are challenges in acquiring brain data, as well as privacy, and security issues that aren’t open to the public. Data preparation is demonstrated, which explains how to convert medical images into regular images. The characteristics of brain data are also discussed; you will encounter many different types of brain data, and the key to developing a well-balanced model is to properly analyze and choose your features. Finally, an overview of brain data research was presented. This article provides information on brain architecture, how to obtain brain data, techniques for obtaining brain data, and an overview of various computer science projects that use brain data. I also hope that readers will find this article useful in advancing their interest in neuroscience by providing a basic understanding of the brain. My current research focuses on employing ANOVA, Information Gain, Random Forest, Forward Selection, and Backward Elimination to determine the most essential characteristics in brain volume.

References

1. The National Institute of Mental Health Data Archive (NDA)
2. Michael S. Tehrani, M.D. (2019) “What’s The Difference Between All the Different Head Scans (X-Ray, Ct, Mri, Mra, Pet Scan)?” MedWell Medical, https://sdbif.org/index/whats-the-difference-between-all-the-different-head-scans/.
3. Matthew Hoffman, MD, Human Anatomy 2021
4. Nelson, C. (2008). Incidental Findings in Magnetic Resonance Imaging (MRI) Brain Research. Journal of Law, Medicine & Ethics, 36(2), 315-319. doi:10.1111/j.1748-720X.2008. 00275.x
5. Positron Emission Tomography: Current Challenges and Opportunities for Technological Advances in Clinical and Preclinical Imaging Systems Juan José Vaquero and Paul Kinahan Annual Review of Biomedical Engineering 2015 17:1, 385-414.
6. Gary H. Glover, Overview of Functional Magnetic Resonance Imaging, Neurosurgery Clinics of North America, Volume 22, Issue 2, 2011, Pages 133-139, ISSN 1042-3680
7. C. W. Schmidt, “CT scans: balancing health risks and medical benefits,” Environmental Health Perspectives, vol. 120, no. 3, pp. a118–a121, 2012.
8. Vang Y.S., Cao Y., Xie X. (2019) A Combined Deep Learning-Gradient Boosting Machine Framework for Fluid Intelligence Prediction. In: Pohl K., Thompson W., Adeli E., Linguraru M. (eds) Adolescent Brain Cognitive Development Neurocognitive Prediction. ABCD-NP 2019. Lecture Notes in Computer Science, vol 11791. Springer, Cham.
9. Kao PY., Zhang A., Goebel M., Chen J.W., Manjunath B.S. (2019) Predicting Fluid Intelligence of Children Using T1-Weighted MR Images and a StackNet. In: Pohl K., Thompson W., Adeli E., Linguraru M. (eds) Adolescent Brain Cognitive Development Neurocognitive Prediction. ABCD-NP 2019. Lecture Notes in Computer Science, vol 11791. Springer, Cham.
10. Luke Guerdan, Peng Sun, Connor Rowland, Logan Harrison, Zhicheng Tang, Nickolas Wergeles, and Yi Shang. Deep Learning vs. Classical Machine Learning: A Comparison of Methods for Fluid Intelligence Prediction. In Adolescent Brain Cognitive Development Neurocognitive Prediction. Springer-Verlag, Berlin, Heidelberg, 17–25.
11. Michael Rebsamen, Christian Rummel, Ines Mürner-Lavanchy, Mauricio Reyes, Roland Wiest, and Richard McKinley. Surface-Based Brain Morphometry for the Prediction of Fluid Intelligence in the Neurocognitive Prediction Challenge 2019. In Adolescent Brain Cognitive Development Neurocognitive Prediction. Springer-Verlag, Berlin, Heidelberg, 26–34.
12. Jose Tamez-Pena, Jorge Orozco, Patricia Sosa, Alejandro Valdes, and Fahimeh Nezhadmoghadam. Ensemble of SVM, Random-Forest and the BSWiMS Method to Predict and Describe Structural Associations with Fluid Intelligence Scores from T1-Weighed MRI. In Adolescent Brain Cognitive Development Neurocognitive Prediction. Springer-Verlag, Berlin, Heidelberg, 47–56.
13. Shikhar Srivastava, Fabian Eitel, and Kerstin Ritter. Predicting Fluid Intelligence in Adolescent Brain MRI Data: An Ensemble Approach. In <i>Adolescent Brain Cognitive Development Neurocognitive Prediction</i>. Springer-Verlag, Berlin, Heidelberg, 74–82.
14. Leo Brueggeman, Tanner Koomar, Yongchao Huang, Brady Hoskins, Tien Tong, James Kent, Ethan Bahl, Charles E. Johnson, Alexander Powers, Douglas Langbehn, Jatin Vaidya, Hans Johnson, and Jacob J. Michaelson. Ensemble Modeling of Neurocognitive Performance Using MRI-Derived Brain Structure Volumes. In <i>Adolescent Brain Cognitive Development Neurocognitive Prediction</i>. Springer-Verlag, Berlin, Heidelberg, 124–132.
15. Neil P. Oxtoby, Fabio S. Ferreira, Agoston Mihalik, Tong Wu, Mikael Brudfors, Hongxiang Lin, Anita Rau, Stefano B. Blumberg, Maria Robu, Cemre Zor, Maira Tariq, Mar Estarellas Garcia, Baris Kanber, Daniil I. Nikitichev, and Janaina Mourão-Miranda. ABCD Neurocognitive Prediction Challenge 2019: Predicting Individual Residual Fluid Intelligence Scores from Cortical Grey Matter Morphology. In <i>Adolescent Brain Cognitive Development Neurocognitive Prediction</i>. Springer-Verlag, Berlin, Heidelberg, 114–123.
16. Tengfei Li, Xifeng Wang, Tianyou Luo, Yue Yang, Bingxin Zhao, Liuqing Yang, Ziliang Zhu, and Hongtu Zhu. Adolescent Fluid Intelligence Prediction from Regional Brain Volumes and Cortical Curvatures Using BlockPC-XGBoost. In <i>Adolescent Brain Cognitive Development Neurocognitive Prediction</i>. Springer-Verlag, Berlin, Heidelberg, 167–175.
17. Yukai Zou, Ikbeom Jang, Timothy G. Reese, Jinxia Yao, Wenbin Zhu, and Joseph V. Rispoli. Cortical and Subcortical Contributions to Predicting Intelligence Using 3D ConvNets. In <i>Adolescent Brain Cognitive Development Neurocognitive Prediction</i>. Springer-Verlag, Berlin, Heidelberg, 176–185.
18. Agata Wlaszczyk, Agnieszka Kaminska, Agnieszka Pietraszek, Jakub Dabrowski, Mikolaj A. Pawlak, and Hanna Nowicka. Predicting Fluid Intelligence from Structural MRI Using Random Forest regression. In <i>Adolescent Brain Cognitive Development Neurocognitive Prediction</i>. Springer-Verlag, Berlin, Heidelberg, 83–91.
19. Juan Miguel Valverde, Vandad Imani, John D. Lewis, and Jussi Tohka. Predicting Intelligence Based on Cortical WM/GM Contrast, Cortical Thickness and Volumetry. In <i>Adolescent Brain Cognitive Development Neurocognitive Prediction</i>. Springer-Verlag, Berlin, Heidelberg, 57–65.
20. Po-Yu Kao, Angela Zhang, Michael Goebel, Jefferson W. Chen, and B. S. Manjunath. Predicting Fluid Intelligence of Children Using T1-Weighted MR Images and a StackNet. In <i>Adolescent Brain Cognitive Development Neurocognitive Prediction</i>. Springer-Verlag, Berlin, Heidelberg, 9–16.
21. Lihao Liu, Lequan Yu, Shujun Wang, and Pheng-Ann Heng. Predicting Fluid Intelligence from MRI Images with Encoder-Decoder Regularization. In <i>Adolescent Brain Cognitive Development Neurocognitive Prediction</i>. Springer-Verlag, Berlin, Heidelberg, 108–113.
22. Huijing Ren, Xuelin Wang, Sheng Wang, and Zhengwu Zhang. Predict Fluid Intelligence of Adolescent Using Ensemble Learning. In Adolescent Brain Cognitive Development Neurocognitive Prediction. Springer-Verlag, Berlin, Heidelberg, 66–73.
23. S. (2020, February 6). What’s the difference between all the different head scans (X-Ray, CT, MRI, MRA, PET scan)? San Diego Brain Injury Foundation. https://sdbif.org/index/whats-the-difference-between-all-the-different-head-scans/
24. Medical Imaging International. (2019, July 24). Posthumous Brain MRI Produces Detailed 3D Images. MedImaging.Net. https://www.medimaging.net/mri/articles/294778751/posthumous-brain-mri-produces-detailed-3d-images.html
25. FMRI Functional Magnetic Resonance Imaging Lab. (n.d.). FMRI Functional Magnetic Resonance Imaging Lab. https://home.csulb.edu/%7Ecwallis/482/fmri/fmri.html
26. Chief, E. I. (2017, October 18). 9 Advantages And Disadvantages Of Pet Scans – ConnectUS. ConnectUS. https://connectusfund.org/9-advantages-and-disadvantages-of-pet-scans.
27. Neuroscience And Computer Science: Combining Knowledge To Improve Humans Lives. (2020, December 15). BuscaEU. https://www.brainlatam.com/blog/neuroscience-and-computer-science-combining-knowledge-to-improve-humans-lives-2360?email=tassia.nunes@brainsupport.co#:~:text=Thus%2C%20applications%20from%20neuroscience%20combined,system%20to%20investigate%20potential%20diseases.
28. FMRI – Advantages and Disadvantages Table In A Level And IB Psychology. (n.d.). fMRI – Advantages and disadvantages table in A Level and IB Psychology. https://getrevising.co.uk/grids/fmri.