Hedonic Hunger: Ghrelin and the brain chemistry behind the epidemic of obesity

These days, fast food is among the most accessible items to order, ready to be served at the touch of a button. It is hard not to get excited about a delicious meal high in fat, sugar and calories, arriving in a matter of minutes. Our insatiable desire for high fat foods has grown along with the portion sizes of meals over the past 50 years. In fact, since the 1970s, the average size of food portions from fast-food chains, restaurants and grocery stores has increased by 138% [1]. Caloric intake has risen significantly from the 1960’s worldwide, and continues to rise on a yearly basis as seen in figure 1. It’s safe to say, eating for pleasure rather than survival has become a trend in today’s age. This pattern has been picked up by researchers, who are now studying how high caloric foods change your brain chemistry, causing you to eat more food than is necessary or recommended [2].

This term is coined hedonic hunger, and it is defined as “the drive to eat in order to obtain pleasure in the absence of an energy deficit” [3]. Hedonic hunger has been pegged as one of the main contributors to the surge in obesity rates in developed countries worldwide [4]. Today, I want to shed some light on one single neuropeptide that may be related to hedonic hunger — ghrelin. This particular neuropeptide has been heavily researched, but is not discussed as often in the public eye. I will be explaining what ghrelin is, how it affects your appetite, brain chemistry, and how food companies use it to keep you coming back for more.

Figure 1. Daily supply of calories between 1961 and 2013. Measured by kilo calories.
Taken from: https://ourworldindata.org/food-supply

Ghrelin is classified as an orexigenic peptide. Essentially, it is a hormone that stimulates appetite or food intake. It operates out of the gastrointestinal tract and when it’s released, it makes you want to eat. However, when you’ve eaten and are full, your stomach stretches out, and stretching of the stomach inhibits the release of ghrelin. This is known as a mechano-sensitive process and can be seen in figure 2 [5]. Ghrelin works in complete opposition to another hormone known as leptin. Leptin is released from adipose tissues (fat deposits) when you are full, signalling something called satiety (the sensation of feeling satisfied from food). These two hormones work together to attenuate or stimulate your appetite. If you stop the release of leptin, this will lead to more constant hunger. If you stop the release of ghrelin, you will not crave food as much [6]. Figure 3 summarizes all of this nicely.

Figure 2. The mechano-sensitive process of ghrelin. As the stomach stretches, it signals the stopping of ghrelin release.
Taken from: http://flipper.diff.org/app/pathways/Ghrelin

Quick facts about Ghrelin:

Ghrelin and sleep:

  • Ghrelin release follows a circadian rhythm:
    • Ghrelin increases before expected meal times
    • Slow, steady increase from midnight to dawn
  • Ghrelin expression is negatively correlated with sleep time:
    • Less sleep = more ghrelin
    • More sleep = less ghrelin
    • Sleep disruption can impede ghrelin rhythms, leading to increased ghrelin levels
      • Sensitive to light levels during sleep phase [7, 8]

Ghrelin and weight:

  • Ghrelin release is inversely proportional to body weight
    • Weight loss = increased ghrelin release
    • Weight gain = decreased ghrelin release
    • From an evolutionary perspective, this is done to make sure your weight does not fluctuate too much
  • Ghrelin release increased with stress – leads to stress eating, which leads to weight gain
Figure 3. Leptin and grelin balance before and after eating.
Image Credit: Designua / Shutterstock

Ok, now that we know more about ghrelin, it is time to talk about how it affects your brain chemistry. Although ghrelin is widely expressed in the peripheral nervous system (outside the brain), there is a particular part of the brain where ghrelin receptors have been found — the reward center. The reward center (also known as the meso-limbic pathway) is modulated by the release of a popular neurotransmitter known as dopamine (DA). The release of this neurotransmitter facilitates positive reinforcement of reward-related activities [9]. Ghrelin receptors have been discovered on the cell bodies of dopaminergic neurons in this area. Thus, whenever ghrelin is released, it actually increases the frequency of DA activity in the meso-limbic pathway, reinforcing the behavior of eating food. Of course this has implications for drug or food addictions, where ghrelin has been shown to modulate addictive behavior through this network. This really shouldn’t come as a shock, but here is where food companies capitalize on this network.

Remember when I said eating food decreases the levels of ghrelin in your body? Well, food companies have figured out how to INCREASE ghrelin levels AFTER eating. Their secret? Very high caloric meals. Here is what happens when you eat food high in calories. Meals that come from fast food restaurants usually contain medium-chain fatty acids (MCFAs). These MCFAs are attached with a precursor for grhelin (des-acyl ghrelin) by an enzyme known as Ghrelin O-Acyl Transferase (GOAT) [10,11]. When GOAT catalyzes these two components, the resulting factor is ghrelin! [10,11,12]. This process is known as posttranslational modification of ghrelin [11,12]. Take a look at figure 4 for reference. If you’ve ever felt hungry right after eating McDonald’s or A&W, this may be a reason why (it happened to me last week, and now I’m writing this article). As a result, increased levels of ghrelin leads to positive reinforcement in the reward center of the brain by influencing dopaminergic neurons. Soon, you may develop motivational behaviors towards these types of food. Again, this is an oversimplified illustration, and there are many other processes at work here. But the point is, over time, food companies have figured out how to carefully engineer food to make it more addictive and tasteful.

Figure 4. Posttranslational modification of ghrelin. The precursors Des-acyl ghrelin and medium-chain fatty acids are catalyzed by GOAT to make ghrelin.
Taken from: Physiological roles of ghrelin on obesity. doi: 10.1016/j.orcp.2013.10.002

In concluding, there is ample evidence pointing towards hedonic eating behavior and the orexigenic peptide ghrelin. Clearly, there is a significant correlation between the rise in calorie rich foods and obesity rates. Obviously ghrelin isn’t the only latent variable here to explain the association, but it may be part of a multivariate answer. Here, we explained the neurochemistry as to how ghrelin can be utilized to make you crave more food, even after satiety. There is a delicate balance that needs to be struck when it comes to these peptides, and any dietary alterations could change eating behaviors.


  1. This is how much portion sizes have changed over time. https://spoonuniversity.com/lifestyle/this-is-how-much-portion-sizes-have-changed-over-time
  2. How sugar and fat trick the brain into wanting more food. https://www.scientificamerican.com/article/how-sugar-and-fat-trick-the-brain-into-wanting-more-food/
  3. Hedonic hunger and binge eating among women with eating disorders. doi: https://doi.org/10.1002/eat.22171
  4. Are We Slaves to Hedonic Hunger? https://psychcentral.com/lib/are-we-slaves-to-hedonic-hunger/
  5. Pathway detail. http://flipper.diff.org/app/pathways/Ghrelin
  6. Ghrelin and Leptin. https://www.news-medical.net/health/Ghrelin-and-Leptin.aspx
  7. Single night of sleep deprivation increased ghrelin levels and feelings of hunger in normal weight healthy men. https://pubmed.ncbi.nlm.nih.gov/18564298/
  8. Light Modulates Leptin and Ghrelin in Sleep-Restricted Adults. doi: 10.1155/2012/530726
  9. Ghrelin at the interface of obesity and reward. doi: 10.1016/B978-0-12-407766-9.00013-4
  10. Structure and Physiological Actions of Ghrelin. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3863518/
  11. Ghrelin – Physiological Functions and Regulation. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5819073/
  12. Ingested Medium-Chain Fatty Acids Are Directly Utilized for the Acyl Modification of Ghrelin. doi: https://doi.org/10.1210/en.2004-0695
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An introduction to functional alignment for fMRI analysis

Previously, we talked about task-based fMRI analysis. In this post, we want to introduce one of the long-standing challenges in task-based fMRI analysis.


Brain decoding, which is a conjunction between neuroscience and machine learning, extracts meaningful patterns (signatures) from neural activities of the human brain [1]. Most of the brain decoding approaches employed functional Magnetic Resonance Imaging (fMRI) technology for visualizing the brain activities because it can provide better spatial resolution in comparison with other imaging techniques [1—4].

fMRI can be used as a proxy to illustrate neural activities by analyzing the Blood Oxygen Level Dependent (BOLD) signals [1]. As one of the most popular supervised techniques in fMRI analysis, Multivariate Pattern (MVP) classification can map neural activities to distinctive brain tasks. MVP can generate a classification (cognitive) model, i.e., decision surfaces, in order to predict patterns associated with different cognitive states. This model can help us to figure out how the human brain works. MVP analysis has an extensive range of applications to seek novel treatments for mental diseases [1—6].

As a fundamental challenge in supervised fMRI studies, the generated MVP models must be generalized and validated across subjects. However, neuronal activities in a multi-subject fMRI dataset must be aligned to improve the performance of the final results. Technically, there are two different kinds of alignment techniques that can be used in harmony, i.e., anatomical alignment and functional alignment. The anatomical alignment as a general preprocessing step in fMRI analysis aligns the brain patterns by using anatomical features, which is extracted from structural MRI in the standard space — including Talairach or Montreal Neurological Institute (MNI). Nevertheless, the performance of anatomical alignment techniques is limited based on the shape, size, and spatial location of functional loci that differ across subjects. In contrast, the functional alignment can directly align the neural activities across subjects, which has been widely used in fMRI studies.


Figure 1. An example of Hyperalignment. Here, subjects watch three categories of visual stimuli. We learn transformation \mathbf{R}^{(i)} for each subject to map the original neural responses to a common space (G).

Most of the recent studies in functional alignment [1—6] have used Hyperalignment (HA) [1]. As Figure 1 depicted, HA refers to the functional alignment of multi-subject data, where shared space is generated from neural activities across subjects. Then, the mapped features can be utilized by MVP techniques in order to boost the performance of the classification analysis. In practice, HA applies a Generalized Canonical Correlation Analysis (GCCA) approach (aka, multi-set CCA) to temporally-aligned neural activities across subjects, where a unique time point must represent the same simulation for all subjects [6].

Let S be the number of subjects, V be the number of voxels (viewed as a 1D vector, even though it corresponds to a 3D volume), and T is the number of time-points in units of Time of Repetitions (TRs). The preprocessed brain image (neural responses) for \ell\text{-}th subject is defined as \mathbf{X}^{(\ell)}\in\mathbb{R}^{V \times T}\text{, }\ell = 1\text{:}S. We consider \mathbf{X}^{(i)} \sim \mathcal{N}(0,1) is normalized by zero-mean and unit-variance in the preprocessing step. Here, \mathbf{X}^{(i)} \text{, } i=1\text{:}S is also time synchronized to provide temporal alignment — i.e., each time point demonstrates the same stimuli for all subjects [6]. In fact, the columns of \mathbf{X}^{(i)} are aligned across subjects by utilizing HA methods. The original HA can be defined as follows where tr() is the trace function [6]:

\underset{\mathbf{R}^{(i)}, \mathbf{G}}{\min} \sum_{i=1}^{S}\| \mathbf{X}^{(i)}\mathbf{R}^{(i)} - \mathbf{G} \|_F^2, \Big(\mathbf{X}^{(\ell)}\mathbf{R}^{(\ell)} \Big)^\top \mathbf{X}^{(\ell)}\mathbf{R}^{(\ell)} = \mathbf{I},

where \ell = 1\dots S, and \mathbf{G} denotes the common space such that:

\mathbf{G} = \sum_{j=1}^{S} \mathbf{X}^{(j)}\mathbf{R}^{(j)}.

In [6], Xu et al. proposed a regularized iterative approach for learning the common space (G) and the transformation matrices \mathbf{R}^{(i)}. Further, Lobert et al. developed a Kernel Hyperalignmenr for nonlinear fMRI analysis [5]. Recently, we also developed Deep Hyperalignment that can scale alignment techniques for large-scale analysis [4].

So, let’s look at the training and testing procedures. Based on the definition, we have a training set \mathbf{X}^{(\ell)}\in\mathbb{R}^{V \times T}\text{, }\ell = 1\text{:}S and a testing set \mathbf{\hat{X}}^{(\ell)}\in\mathbb{R}^{V \times T}\text{, }\ell = 1\text{:}\hat{S} — where \hat{S} is the number of subject in the testing set. In the training procedure, we first learn a common space \mathbf{G} and a set of transformation matrices \mathbf{R}^{(\ell)}\text{ for }\ell=1:S. Then, we generate a classification model by using \mathbf{X}^{(\ell)}\mathbf{R}^{(\ell)}\text{ for }\ell=1:S. In the testing stage, we learn the transformation matrices, where the shared space will not be updated anymore. We actually use following objective function:

\underset{\mathbf{\hat{R}}^{(i)}}{\min} \sum_{i=1}^{S}\| \mathbf{\hat{X}}^{(i)}\mathbf{\hat{R}}^{(i)} - \mathbf{G} \|_F^2,

Finally, the performance of the trained model can be evaluated by using the transformed testing features \mathbf{\hat{X}}^{(\ell)}\mathbf{\hat{R}}^{(\ell)}\text{ for }\ell=1:\hat{S}. This learning procedure is almost the same in most alignment techniques.

Shared Response Model (SRM)

Figure 2: Graphical model for SRM. Shaded nodes: observations, unshaded nodes: latent variables, and black squares: hyperparameters [7].

SRM is a probabilistic extension of the Hyperalignment [7] — i.e., SRM uses probabilistic CCA for generating the shared space (aka, common space). Let m be the number of subjects in a preprocessed fMRI dataset, d denotes the number of time points in TRs, and v is the number of voxels. fMRI time-series for i-th subject denotes by \mathbf{X}_{i} \in \mathbb{R}^{v \times d} for i=1:m. SRM’s objective function is to model each subject’s neural responses as \mathbf{X}_{i} = \mathbf{W}_{i}\mathbf{S} + \mathbf{E}_{i} , where \mathbf{W}_{i} \in \mathbb{R}^{v \times k} denotes a basis of topographies for subject i, k is the number of selected features, \mathbf{S} \in \mathbb{R}^{k \times d} is the shared space. SRM’s objective function for all subjects can be also written as follows: [7]

\underset{\mathbf{S,}\mathbf{W}_{i}}{\min} \sum_{i=1}^{m} \| \mathbf{X}_{i} - \mathbf{W}_{i}\mathbf{S} \|_F^2, \text{subject to } \mathbf{W}_{i}^{\top}\mathbf{W}_{i}=\mathbf{I}_k,

where \|. \|_F denotes the Frobenius norm, and \mathbf{I}_k is identity matrix in size k. Further, we calculate the shared space as follows:

\mathbf{S} = \frac{1}{m}\sum_{i=1}^{m} \mathbf{W}_{i}^{\top} \mathbf{X}_{i}.

Figure 2 shows the graphical model for SRM — where a probabilistic optimization approach is used to learn a shared space \mathbf{S} and the basis of topographies \mathbf{W}_{i}. In this figure, \mathbf{s}_{t} \in \mathbb{R}^{k} with covariance \mathbf{\Sigma}_{s} is a hyperparameter modeling the shared response at time t=1:d, \mathbf{x}_{it}\in \mathbb{R}^{v} denotes the observed pattern of voxel responses for the i-th subject at time t, \mathbf{\rho}^{2}_{i} is i-th subject independent hyperparameter, and \mathbf{\mu}_{i} denotes the subject specific mean. The final optimization procedure is explained in Section 3.1 of [7].

Supervised Hyperalignment (SHA)

Figure 3. Comparison of different HA algorithms for aligning neural activities [2].

We recently illustrated that the performance of HA methods might not be optimum for supervised fMRI analysis (i.e., MVP problems) because they mostly employed unsupervised GCCA techniques for aligning the neural activities across subjects [2, 3]. Therefore, we have developed Local Discriminant Hyperalignment (LDHA) [3] and then Supervised Hyperalignment (SHA) [2] for improving the alignment accuracy in the MVP problems. Although LDHA can improve the performances of both functional alignment and MVP analysis, its objective function cannot directly calculate a supervised shared space and still uses the classical unsupervised shared space [3]. Thus, it cannot provide stable performance and acceptable runtime for large datasets in real-world applications [2].

Figure 3 compares the main difference between unsupervised HA, LDHA, and SHA. As depicted in this figure, two subjects watch two photos of houses, as well as two photos of bottles — where [\mathbf{H1}, \mathbf{B1}, \mathbf{H2}, \mathbf{B2}], shows the sequence of stimuli (after temporal alignment). Here, the shared spaces can be calculated by employing different correlations between neural activities. Figure 3.a demonstrates that the unsupervised HA just maximizes the correlation between the voxels with the same position in the time series because it does not use the supervision information.

Figure 3.b illustrates the LDHA approach, where it utilizes the unsupervised shared space for the alignment problem. Indeed, the main issue in LDHA objective function is that the covariance matrices cannot decompose to the product of a symmetric matrix [2]. In order to calculate the shared space in LDHA, each pair of stimuli must be separately compared with each other, and the shared space is gradually updated in each comparison (see Algorithm 2 in [3]). Therefore, LDHA cannot use a generalized optimization approach (such as GCCA) and its time complexity is not efficient for large datasets.

As shown in Figure 3.c, SHA consists of two main steps:

  1. Generating a supervised shared space, where each stimulus is only compared with the shared space to align the neural activities;
  2. Calculating the mapped features in a single iteration.

The neural activities belong to \ell\text{-}th subject can be denoted by \mathbf{X}^{(\ell)} \in \mathbb{R}^{T\times V}\text{, } \ell=1\text{:}S (defined same as the Hyperalignment section) and the class labels that are denoted by \mathbf{Y}^{(\ell)}=\{{y}_{mn}^{(\ell)} \}\text{, }\mathbf{Y}^{(\ell)}\in\{0, 1\}^{L\times T}\text{, } m=1\text{:}L\text{, }n=1\text{:}T\text{, }L>1. Here, L is the number of classes (stimulus categories). In order to infuse supervision information to the HA problem, this medod defines a supervised term as follows:

\mathbf{K}^{(\ell)} \in \mathbb{R}^{L \times T} = \mathbf{Y}^{(\ell)}\mathbf{H},

where the normalization matrix \mathbf{H}\in\mathbb{R}^{T\times T} is denoted as follows:

\mathbf{H} = \mathbf{I}_{T} - \gamma{\mathbf{1}}_{T},

where {\mathbf{1}}_{T} \in {1}^{T\times T} denotes ones matrix in size T, and \gamma makes a trade-off between within-class and between-class samples. Objective function of SHA is defined as follows:

\underset{\mathbf{W}, \mathbf{R}^{(i)}}{\min}\{\sum_{i = 1}^{S} \| \mathbf{K}^{(i)}\mathbf{X}^{(i)}\mathbf{R}^{(i)} - \mathbf{W}\|^2_F\}, (\mathbf{R}^{(\ell)})^\top((\mathbf{K}^{(\ell)}\mathbf{X}^{(\ell)})^\top\mathbf{K}^{(\ell)}\mathbf{X}^{(\ell)} + \epsilon \mathbf{I}_V) \mathbf{R}^{(\ell)}=\mathbf{I}_{V},

where \ell=1\text{:}S, \epsilon is a regularization term, \mathbf{R}^{(\ell)} denotes the mapping matrices, and \mathbf{W} \in \mathbb{R}^{L\times V} is supervised shared space — such that:

\mathbf{W} = \frac{1}{S} \sum_{j=1}^{S} \mathbf{K}^{(j)}\mathbf{X}^{(j)}\mathbf{R}^{(j)}.

We then show that supervised shared space can be calculated directly as follows: [2]

\underset{\mathbf{W}, \mathbf{R}^{(i)}}{\min}\{\sum_{i = 1}^{S} \|\mathbf{K}^{(i)}\mathbf{X}^{(i)}\mathbf{R}^{(i)} - \mathbf{W} \|^2_F\} \equiv \underset{\mathbf{W}}{\min}\{\text{tr}(\mathbf{W}^\top\mathbf{U}\mathbf{W})\}, \text{subject to }\mathbf{R}^{(\ell)}=((\mathbf{K}^{(\ell)}\mathbf{X}^{(\ell)})^\top\mathbf{K}^{(\ell)}\mathbf{X}^{(\ell)} + \epsilon\mathbf{I}_V\Big)^{-1}\big(\mathbf{K}^{(\ell)}\mathbf{X}^{(\ell)})^\top\mathbf{W}, \mathbf{U} = \sum_{\ell=1}^{S}\mathbf{I}_{L} - \mathbf{K}^{(\ell)}\mathbf{X}^{(\ell)}((\mathbf{K}^{(\ell)}\mathbf{X}^{(\ell)})^\top\mathbf{K}^{(\ell)}\mathbf{X}^{(\ell)}+\epsilon\mathbf{I_{V}})^{-1}\mathbf{K}^{(\ell)}\mathbf{X}^{(\ell)})^\top.

Here, \mathbf{W} is the right eigenvectors of \mathbf{U} [2, 4]. Further, the unsupervised shared space for the testing stage is calculated as follows: [2]

\mathbf{G} = \frac{1}{S}\Big(\sum_{\ell=1}^{S}\mathbf{W}^T\mathbf{K}^{(\ell)}\Big)^\top.

Indeed, the testing phase for SHA is the same as unsupervised HA. The only difference between SHA and unsupervised HAs lies in the procedure of generating the shared space in the training phase.


One of the main challenges in fMRI studies, especially Multivariate Pattern (MVP) analysis, is using multi-subject datasets. On the one hand, the multi-subject analysis is necessary to estimate the validity of the generated results across subjects. On the other hand, analyzing multi-subject fMRI data requires accurate functional alignment between neuronal activities of different subjects for improving the performance of the final results. Hyperalignment (HA) is one of the most significant functional alignment methods, which can be formulated as a CCA problem for aligning neural activities of different subjects to a common/shared space. HA techniques can use different optimization solutions for generating an adequate shared space — classic CCA (in HA), probabilistic CCA (in SRM), and supervised methods (in LDHA and SHA). In the future, we will describe the related math background for alignment techniques and explain some challenging issues that may happen during the analysis.


  1. Decoding Neural Representational Spaces Using Multivariate Pattern Analysis. DOI: https://doi.org/10.1146/annurev-neuro-062012-170325
  2. Supervised Hyperalignment for multi-subject fMRI data alignment. DOI: 10.1109/TCDS.2020.2965981
  3. Local Discriminant Hyperalignment for multi-subject fMRI data alignment. Link: https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/download/14347/13743
  4. Deep Hyperalignment. Link: https://papers.nips.cc/paper/6758-deep-hyperalignment.pdf
  5. Kernel Hyperalignment. Link: https://papers.nips.cc/paper/4592-kernel-hyperalignment.pdf
  6. Regularized hyperalignment of multi-set fMRI data. DOI: 10.1109/SSP.2012.6319668
  7. A Reduced-Dimension fMRI Shared Response Model. Link: https://papers.nips.cc/paper/5855-a-reduced-dimension-fmri-shared-response-model
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Blue mice and pink elephants: Why alcohol is among the worst drugs for dependence and withdrawal

Most of us have been there. The next morning after a night of alcohol consumption is usually met with headaches, nausea, sleep deprivation, dizziness, dry mouth and excessive thirst. The reason for some of these symptoms can be attributed to the bodies fast response to the presence, and excretion of excessive alcohol. Drinking large amounts of alcohol over the course of an evening allows for the body and the brain to briefly adjust its homeostasis to adapt to increased levels of alcohol, operating at a “new normal”. When you abruptly stop drinking, your body and brain chemistry must now normalize on its own, and this leads to the symptoms mentioned above. But what happens when you drink excessively over long periods of time (i.e. months), forcing a homeostatic shift to the point where your body and brain become reliant and tolerant to alcohol? In this article, I will be explaining why alcohol may be one of the worst drugs to come off of.

Alcohol is considered a depressant, which refers to a class of drugs that inhibit or depress the central nervous system (CNS). Neurotransmission of gamma-Aminobutyric acid (GABA) — a neurotransmitter that works to inhibit neuronal excitability in the CNS — is increased during the consumption of alcohol. By increasing the availability of GABA, behaviors such as decreased attention, relaxation, alternation in memory, dizziness (alcohol reduces the viscosity of your inner ear fluid, messing with your balance) and drowsiness become apparent during a night of heavy drinking [1].

But unlike most other drugs, alcohol also suppresses the neurotransmission of an excitatory neurotransmitter known as glutamate. Glutamate is the major excitatory neurotransmitter in the brain and it is responsible for many important cognitive functions. If inhibited, it can also cause sedative effects [2]. Together, these dual processes decrease the flow of calcium, which is central for control of cell excitability and neurotransmitter release [3, 4]. So, to summarize, alcohol acts as a depressant through two main pathways. It increases the availability and activity of GABA (an inhibitory neurotransmitter), and it decreases the activity of glutamate (an excitatory neurotransmitter) [4]. This leads to an overall mass depression of the CNS.

Figure 1. Alcohol dependence and withdrawal. The behavioral symptoms above are brought on by alterations in inhibitory and excitatory systems in the central nervous system [4,5].

Over long periods of significant alcohol consumption, your brain tries to restore its equilibrium (homeostasis) by fine-tuning the receptor functions for GABA and glutamate. This results in a decreased sensitivity for alcohol, which means you now need more alcohol to achieve the same effect on the body and brain as before. This is known as pharmacodynamic tolerance. As a result, your CNS compensates for the increased GABA by reducing GABA reception functions and up-regulates glutamate receptors due to a lack of glutamate [2,4]. When alcohol is abruptly reduced or discontinued, that’s when things go from bad to worse.

The moment your CNS is devoid of alcohol, mass hyper-excitability of neuronal firing occurs (known as sympathetic overdrive or autonomic hyperactivity). GABA receptors, which were previously down-regulated to reduce neurotransmission can no longer inhibit cellular function properly anymore. Additionally, glutamate receptors are now unregulated, which leads to a much higher flow of Ca2+ and this can be highly toxic for cellular functioning [4]. As a result, you will now start feeling symptoms opposite of sedative effects you felt before. This is known as the “rebound effect” and can be visualized in the figure below.

Figure 2. Relationship between drug tolerance and withdrawal. The same adaptive neurophysiological changes that develop in response to drug exposure and produce drug tolerance manifest themselves as withdrawal effects once the drug is removed. As these changes develop, tolerance increases; as they subside, the severity of the withdrawal effects decreases [6].

An individual coming off of long term alcohol abuse may experience severe symptoms such as paranoia, altered sensations, delusions and worst of all, Delirium Tremens (DT). DT is a set of symptoms that include seizures, tremors, psychosis, vivid hallucinations (often terrifying), and even death [7,8]. Seeing “pink elephants” and “blue mice” serve as euphemisms for the severe hallucinations one may experience when withdrawing from alcohol.

To conclude, the rebound effects of alcohol are dangerous because of its parallel effects on GABA and glutamate neurotransmitters and their receptors. Upon consumption, alcohol initially enhances inhibitory receptor function (GABA increases) and decreases excitatory function in the brain (glutmate decreases). Sedation, relaxation, decreased attention, and memory loss are a result of initial alcohol consumption. This leads to the development of neural changes to offset the drug effect in the CNS (trying to achieve balance of these systems). When the drug is no longer available, GABA receptors are greatly diminished (GABA decreases) and glutamate receptors (glutamate increases) are amplified, leading to an overactive CNS. This results in symptoms such as tremors, anxiety, psychosis, seizures, convulsions and DT. Unlike some other drugs, alcohol simultaneously disrupts both inhibitory and excitatory receptor functions in such a way that when alcohol use ceases, these unregulated mechanisms result in mass hyperactivity [8].


  1. Role of Acetaldehyde in Mediating the Pharmacological and Behavioral Effects of Alcohol. doi: https://doi.org/10.1016/j.pharmthera.2006.02.001
  2. What Alcohol Really Does to Your Brain. https://www.forbes.com/sites/daviddisalvo/2012/10/16/what-alcohol-really-does-to-your-brain/#4e06effc664e
  3. Calcium influx during an action potential. doi: https://doi.org/10.1016/S0076-6879(98)93023-3
  4. Alcohol and neurotransmitter interaction. https://pubs.niaaa.nih.gov/publications/arh21-2/144.pdf
  5. Alcohol dependence and withdrawal. https://www.youtube.com/watch?v=1RxATXURxQM
  6. Biopsychology, 9th edition, Chapter 15: Drug addiction and the Brain’s Reward Circuits.
  7. Recognition and management of withdrawal delirium (delirium tremens). doi:10.1056/NEJMra1407298
  8. Alcohol, benzos and opiates — Withdrawal that might kill you. https://www.psychologytoday.com/ca/blog/all-about-addiction/201001/alcohol-benzos-and-opiates-withdrawal-might-kill-you

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Task-based fMRI analysis

The past two decades have seen significant advances in computational neuroscience [1]. 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 [1].

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 [2]. 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. [2].

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) [1].

Task-based fMRI can be analyzed with two different perspectives — viz, predictive methods, and similarity analysis [3]. 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 [5].

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].


  1. Decoding Neural Representational Spaces Using Multivariate Pattern Analysis. DOI: https://doi.org/10.1146/annurev-neuro-062012-170325
  2. Supervised Hyperalignment for multi-subject fMRI data alignment. DOI: 10.1109/TCDS.2020.2965981
  3. Multi-Objective Cognitive Model: a supervised approach for multi-subject fMRI analysis. DOI: 10.1007/s12021-018-9394-9 
  4. 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
  5. Matching categorical object representations in inferior temporal cortex of man and monkey. DOI: https://dx.doi.org/10.1016%2Fj.neuron.2008.10.043
  6. Local Discriminant Hyperalignment for multi-subject fMRI data alignment. Link: https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/download/14347/13743
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