This is part of a series on Analysis Posts on Long COVID and ME/CFS

Back Story

I’d love some additional help, please. I’ve done two BiomeSight.com tests. I followed the suggestions after the first test and my microbiome has changed and some of my symptoms are improving. However, I couldn’t tolerate any of the bifidobacterium strains I tried, all of them caused very painful long-lasting migraines. Despite taking them for a combined 6wks (3 different strains for 2wks each), my bifidobacterium levels look unchanged. The suggestions do say that ‘No Probiotics without some adverse risks could not be identified.’ so maybe it’s better I just avoid them altogether for now?

• I was diagnosed with ME/CFS 16yrs ago, after EBV 22yrs ago. 
• I caught Covid-19 in 2023.
• I was diagnosed with chronic migraines in 2024 – they have increased in severity and occurrence over the last 5yrs, since the Covid-19 vaccines, though I can’t be sure it’s related.
• My primary symptoms are: fatigue, pem, migraines, brain fog, ibs, acne, and hair loss. 

I give my permission to use the above information anonymously for a blog post.

Analysis

I smiled when I saw ” ‘No Probiotics without some adverse risks could not be identified” and “I couldn’t tolerate any of the bifidobacterium strains I tried“. It seems that the expert system are making good (probable) suggestions. Suggestions are based on odds and not guaranteed.

Pass 1 – Based on Reported Symptoms

When there are many symptoms, my usual path is to get symptoms entered and then get suggestions focused on the bacteria likely associated to those symptoms. This is a targeted approach.

This person had entered any symptoms for their latest sample, and did for the sample from 7 months prior. 4-9 months between samples is what I advocate (balancing costs and time to change the microbiome).

I usually check all of the types of suggestions (I have no ideological position against using any of the types)

Then on the resulting page we see 12 bacteria that are the most likely causes. 2 low and 10 high. Suggestions are computed using five(5) different algorithms and then we use Monte Carlo Model to improve the odds of making good choices. Why different algorithms — simple, microbiome tests are fuzzy in their identification and many different criteria for selecting bacteria are advocated in the literature.

We go to the Consensus Suggestions and sort by Take Count — to get what all agrees about.

Looking at positive 5’s only:

• Vitamins
  • Vitamin B2
  • Vitamin B1
  • Zinc
• Amino Acid
  • Melatonin
  • Carnitine
  • Glutamine
  • Taurien
• Antibiotic (Only 5’s)
  • loperamide hydrochloride  Loperamide is most commonly used to treat acute and chronic diarrhea, including traveler’s diarrhea and diarrhea associated with inflammatory bowel disease (IBD).
  • florfenicol.  Florfenicol is effective against a wide range of bacterial pathogens in animals, including both Gram-positive and Gram-negative bacteria. It is commonly used to treat respiratory infections, gastrointestinal infections, urinary tract infections, and other bacterial infections in livestock and companion animals
  • Atorvastatin Atorvastatin belongs to a class of medications known as statins, which work by inhibiting HMG-CoA reductase, an enzyme involved in cholesterol synthesis. By reducing cholesterol production in the liver, atorvastatin helps lower total cholesterol, LDL cholesterol (often referred to as “bad” cholesterol), and triglyceride levels.
• Common Supplements and Herbs
  • Quercetin
  • {Nobiletin (oranges and lemons)}
  • Luteolin
  • Gallate (Gallic acid)
  • Epicatechin
  • Rosemary
  • Bitter Gourd
  • Camellia
  • Gingko
  • Chitooligosaccharides
  • Cannabinoids
• Diet. I usually ignore because of the lack of precision. Usually I keep to foods
  • Low fiber diet, Low fat, high-complex carbohydrate diet
• Foods: This gives better guidance
  • Mulberry
  • Blueberry
  • Chokeberry
  • Lemon
  • Broccoli
  • Cabbage
  • Dark Greens
  • Doenjang
  • Rice (a 4 – 0 )
  • These two should be done with caution because of probiotic bacteria in them
    • Kimchi
    • Kefir
• Probiotics
  • Lactobacillus mucosae (Not available retail)
  • Bifidobacterium longum subsp. longum BB536 {BB536}
  • Lacticaseibacillus rhamnosus {l. rhamnosus}
  • Lentilactobacillus kefiri {Kefibios} — available in Italy only at present
  • Ligilactobacillus salivarius {L. salivarius}
  • Lacticaseibacillus paracasei {L.paracasei}
• Sugars
  • Chitosan
  • Lactulose

It is interesting that Lactobacillus dominate with just one Bifidobacterium. I would carefully try these, one at a time, starting with a low dosage and increases, then change every 1-2 week to the next (keeping notes!!!), My preferred source of probiotics are listed here.

Pass 2 – Based on PubMed

I view this method as less accurate but the suggestions are ideal for discussion with a MD if antibiotics or other prescription items are suggested. It is available as the last item.

Rather than detailing items, I attached the report below

Treatment Suggestions for PersonalHealthInformation@Restricted.EUDownload

Feedback of Above

Thank you very much! That’s incredibly helpful.

I’ll give this new round of suggestions a go, and then I’ll do another test.

I don’t have a willing GP (or vet, lol) to prescribe antibiotics but it’s very interesting that statins suggested – high cholesterol runs in my family and a lot of them are on statins. 

The cholesterol issues are often DNA related… and DNA also impacts the microbiome. DNA is hard to change, the microbiome is easier.

From Perplexity:
High cholesterol levels can indeed be influenced by genetic factors, with both common and rare gene variants playing significant roles in LDL cholesterol regulation. Here’s a breakdown of the genetic mechanisms involved:

Key Genes Affecting Cholesterol

1. LDLR (LDL Receptor)
   Mutations in this gene (chromosome 19) disrupt LDL cholesterol clearance, causing familial hypercholesterolemia (FH). This autosomal dominant condition leads to lifelong elevated LDL levels (200–300% higher in heterozygotes) due to defective receptor production or function126.
2. APOB (Apolipoprotein B)
   Mutations in APOB impair LDL binding to receptors, reducing clearance. For example, the APOB variant causing “familial ligand-defective apoB-100” increases LDL by 200–300%17.
3. PCSK9
   Gain-of-function mutations in this gene degrade LDL receptors excessively, raising LDL levels. Conversely, loss-of-function variants (e.g., in 2% of African Americans) lower LDL by 30% and protect against heart disease168.
4. APOE (Apolipoprotein E)
   Common isoforms (E2, E3, E4) influence LDL levels:
   • E4 carriers have ~5% higher LDL due to rapid lipoprotein clearance and LDLR downregulation.
   • E2 carriers have ~5% lower LDL but risk familial dysbetalipoproteinemia13.

Inherited Disorders

• Familial Hypercholesterolemia (FH):
  Caused by mutations in LDLR, APOB, or PCSK9. Affects ~1/250 people, leading to LDL >190 mg/dL and premature atherosclerosis if untreated146.
• Familial Hypobetalipoproteinemia:
  APOB mutations reduce LDL production, resulting in very low cholesterol levels13.
• Autosomal Recessive Hypercholesterolemia:
  Rare ARH mutations cause LDL receptor dysfunction, leading to severe cholesterol elevation1.

Polygenic Influences

Most hypercholesterolemia cases involve interactions between multiple common variants (e.g., APOE, NPC1L1) and lifestyle factors. These variants individually exert small effects but collectively contribute to cholesterol variability137.

While genetics set baseline risks, diet and exercise remain critical for management, especially in individuals with predisposing variants368. Genetic testing is recommended for suspected FH to guide early intervention

Postscript – and Reminder

I am not a licensed medical professional and there are strict laws where I live about “appearing to practice medicine”.  I am safe when it is “academic models” and I keep to the language of science, especially statistics. I am not safe when the explanations have possible overtones of advising a patient instead of presenting data to be evaluated by a medical professional before implementing.

I cannot tell people what they should take or not take. I can inform people items that have better odds of improving their microbiome as a results on numeric calculations. I am a trained experienced statistician with appropriate degrees and professional memberships. All suggestions should be reviewed by your medical professional before starting.

The answers above describe my logic and thinking and is not intended to give advice to this person or any one. Always review with your knowledgeable medical professional.

First, apologies to people over the microbiome prescription site being up, then down, then up, then down. The hosting company that I am using (and 900,000 other customers!) having been dealing with issues with their cloud provider. As I write this on Saturday, March 8th 2025, evening — it is back up.

Today, I reworked some old page concepts, improving the mathematics and the presentation. The purpose is to give you some ideas of where your ME/CFS or Long COVID may progress. By progress, I mean symptoms that may get added to your already massive list.

To get to this feature, just go to the menu bar and select Symptom Associations

This will show a page with no symptoms/characteristics entered.

Enter the most critical symptom that you have. For this example, I will do long COVID. Just enter it in the Search box until you see what you are interested in

Check the Check box and the page will refresh. You will see that 11.7% of the samples report Long Covid. Below it are the OTHER symptoms that these people report — with the percentage that reports each symptom

We will pick POTS next. The page will update. Note that Post exertional Malaise that was 26% chance above jumps to 67%. Having POTS with Long COVID increases the odds.

Adding in General Headaches, increases Brain Fog to 84% chance. If you do not have Brain Fog at the moment, there is a very good chance that you will get it.

Bottom Line

The purpose of this tool is give concrete odd of what your next symptoms may be. Here’s a walk through.

For any one that is interested, bacteria with P < 0.005 significance to 324 symptoms and diagnosis is now available (with source data) at https://microbiomeprescription.com/sample/Frequency
Some items of interest to the ME/CFS Community are below

In my last post on MRI Scans, I felt the best model is based on Evidence of widespread metabolite abnormalities in Myalgic encephalomyelitis/chronic fatigue syndrome: assessment with whole-brain magnetic resonance spectroscopy [2020]. Metabolite abnormalities can be a direct result of microbiome dysfunctions. Those abnormalities are very treatable using microbiome tests and expert systems such as generated by Microbiome Prescription.

What are Metabolites?

Metabolites are substances made or used in the body during metabolism, which is the process of breaking down food or chemicals into energy and other useful materials. They help the body grow, repair itself, and function properly. Examples include amino acids, vitamins, and sugars.

Example for ME/CFS

In Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), metabolites have been found to play a critical role in understanding the disease’s mechanisms and symptoms:

1. Gut Microbiome and Butyrate: ME/CFS is associated with changes in gut bacteria, leading to reduced levels of butyrate, a metabolite produced by certain gut microbes. Butyrate supports gut health, immune regulation, and energy production. Reduced butyrate levels in ME/CFS patients are linked to fatigue severity and inflammation.
2. Energy Metabolism: Studies reveal abnormalities in pathways like fatty acid metabolism, glucose metabolism, and the citric acid (TCA) cycle in ME/CFS patients. These changes suggest impaired cellular energy production, contributing to chronic fatigue.
3. Amino Acid Metabolism: Altered tryptophan metabolism and disruptions in the kynurenine pathway have been observed, which may affect immune function and contribute to neurocognitive symptoms through the gut-brain axis.
4. Plasma Metabolites: ME/CFS patients exhibit differences in plasma metabolites compared to healthy controls, particularly after physical exertion. These include disruptions in glutamate metabolism, which may impact recovery and exacerbate symptoms.
5. Disease Subtypes: Metabolomic studies have identified distinct metabolic profiles among ME/CFS patients, suggesting subtypes with different clinical presentations and underlying mechanisms.

These findings highlight the importance of metabolites in ME/CFS research, offering potential biomarkers for diagnosis and targets for therapeutic interventions.

Example for IBS

In the context of Irritable Bowel Syndrome (IBS), metabolites play a significant role:

1. Gut microbiota-derived metabolites: These are substances produced by the bacteria in our intestines and are thought to be involved in IBS symptoms. Some important examples include:
   • Bile acids
   • Short-chain fatty acids
   • Vitamins
   • Amino acids
   • Serotonin
   • Hypoxanthine
2. Blood metabolites: Certain metabolites in the blood have been found to have a causal relationship with IBS. For example:
   • Stearate: Associated with decreased susceptibility to IBS
   • Arginine: Associated with increased risk of IBS
   • 1-palmitoylglycerol: Associated with increased risk of IBS
3. Fecal metabolites: Studies have identified specific fecal metabolite profiles in IBS patients that differ from healthy individuals. These metabolites are often amino acids or fatty acids.
4. Brain-gut interaction: Some metabolites, particularly amino acids like tryptophan, glutamate, and histidine, may influence brain function in IBS patients5. They could affect brain connectivity either directly by crossing the blood-brain barrier or indirectly through peripheral mechanisms.

Understanding these metabolites and their interactions with the gut microbiome may provide valuable insights into the underlying mechanisms of IBS and potentially lead to new diagnostic tools or treatments.

Enzymes Role to Metabolites

Enzymes play a crucial role in managing metabolites within our bodies. Here’s a simple description of their relationship:

1. Enzymes are proteins that act as biological catalysts7. They speed up chemical reactions in our cells without being used up themselves.
2. Metabolites are substances produced or used during metabolism1. They can be small molecules like sugars, amino acids, or fatty acids.
3. Enzymes help break down large molecules (like proteins, fats, and carbohydrates) into smaller metabolites. This process is essential for digestion and energy production.
4. Enzymes also help build larger molecules from smaller metabolites. This is important for creating cellular structures and storing energy.
5. Each enzyme typically works on specific metabolites, called substrates1. The enzyme and substrate fit together like a lock and key.
6. By controlling which reactions happen and how quickly, enzymes regulate the levels of various metabolites in our bodies. This helps maintain balance and allows cells to respond to changing needs.

In essence, enzymes are the workers that manage metabolites, ensuring our bodies can efficiently use the food we eat and carry out the chemical processes necessary for life.

Data From Samples Uploaded with ME/CFS

It happens that from uploaded samples and KEGG: Kyoto Encyclopedia of Genes and Genomes; we can determine that the following enzymes are (VERY VERY) statistically significant. The most significant ones are all too high. The top ones comes from the three genus only: Chlorobaculum , Pelodictyon and Prosthecochloris

• Chlorobaculum limnaeum
• Chlorobaculum parvum
• Chlorobaculum tepidum
• Chlorobium chlorochromatii
• Chlorobium limicola
• Chlorobium phaeobacteroides
• Chlorobium phaeovibrioides
• Chloroherpeton thalassium
• Pelodictyon luteolum
• Pelodictyon phaeoclathratiforme
• Prosthecochloris aestuarii
• Prosthecochloris sp. CIB 2401
• Prosthecochloris sp. GSB1
• Prosthecochloris sp. HL-130-GSB

Some (but not all) enzymes can be provided by some probiotics. Below is recent feedback from a person dealing with a child’s autism.

EC Key	Enzyme Name	Probability	Shift
1.1.1.325	sepiapterin reductase (L–threo-7,8-dihydrobiopterin forming)	1.61685e-015	high
2.1.1.331	bacteriochlorophyllide d C-121-methyltransferase	1.61685e-015	high
2.1.1.332	bacteriochlorophyllide d C-82-methyltransferase	1.61685e-015	high
2.1.1.333	bacteriochlorophyllide d C-20 methyltransferase	1.61685e-015	high
3.1.1.100	chlorophyllide a hydrolase	1.61685e-015	high
4.2.1.169	3-vinyl bacteriochlorophyllide d 31-hydratase	1.61685e-015	high
2.3.3.8	ATP citrate synthase	1.18208e-013	high
1.3.1.75	3,8-divinyl protochlorophyllide a 8-vinyl-reductase (NADPH)	5.353e-013	high
2.5.1.42	geranylgeranylglycerol-phosphate geranylgeranyltransferase	7.71322e-013	high
1.17.98.2	bacteriochlorophyllide c C-71-hydroxylase	2.69579e-012	high
2.7.8.36	undecaprenyl phosphate N,N′-diacetylbacillosamine 1-phosphate transferase	3.60014e-009	low
1.11.1.6	catalase	1.37633e-008	low
6.5.1.8	3′-phosphate/5′-hydroxy nucleic acid ligase	1.02548e-007	low
2.5.1.105	7,8-dihydropterin-6-yl-methyl-4-(β-D-ribofuranosyl)aminobenzene 5′-phosphate synthase	1.27364e-007	low
1.1.1.65	pyridoxine 4-dehydrogenase	1.89183e-007	high
2.6.1.59	dTDP-4-amino-4,6-dideoxygalactose transaminase	3.34948e-007	low
4.1.1.31	phosphoenolpyruvate carboxylase	4.73602e-007	high
5.4.99.26	tRNA pseudouridine65 synthase	5.94186e-007	high
1.1.1.127	2-dehydro-3-deoxy-D-gluconate 5-dehydrogenase	8.09119e-007	low
3.4.21.83	oligopeptidase B	8.612e-007	high
1.1.1.9	D-xylulose reductase	1.07212e-006	high
1.12.1.4	hydrogenase (NAD+, ferredoxin)	1.22627e-006	high
3.5.2.9	5-oxoprolinase (ATP-hydrolysing)	1.30156e-006	high
2.7.1.12	gluconokinase	1.49961e-006	high
1.6.1.2	NAD(P)+ transhydrogenase (Re/Si-specific)	3.05641e-006	high
7.1.1.1	proton-translocating NAD(P)+ transhydrogenase	3.05641e-006	high
3.1.1.114	methyl acetate hydrolase	3.98055e-006	low
3.2.1.165	exo-1,4-β-D-glucosaminidase	4.31375e-006	low
2.3.1.117	2,3,4,5-tetrahydropyridine-2,6-dicarboxylate N-succinyltransferase	4.31566e-006	high
2.7.8.12	teichoic acid poly(glycerol phosphate) polymerase	4.59165e-006	low
6.1.1.13	D-alanine—poly(phosphoribitol) ligase	4.88544e-006	low
1.12.98.4	sulfhydrogenase	6.48299e-006	low
4.2.1.22	cystathionine β-synthase	7.49383e-006	high
2.3.1.78	heparan-α-glucosaminide N-acetyltransferase	8.1071e-006	low

This is an update of my post from 10 years ago, CFS: Appropriate Brain Scans. I will focus on studies in those 10 years. Short version of these studies below.

Data showed that MRI studies frequently reported structural changes in the white and gray matter. Abnormalities of the functional connectivity within the brainstem and with other brain regions have also been found. The studies have suggested possible mechanisms including astrocyte dysfunction, cerebral perfusion impairment, impaired nerve conduction, and neuroinflammation involving the brainstem, which may at least partially explain a substantial portion of the ME/CFS symptoms and their heterogeneous presentations in individual patient
Brainstem Abnormalities in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: A Scoping Review and Evaluation of Magnetic Resonance Imaging Findings [2021]

In our family dealing with ME/CFS, we were fortunate is having brain scans done by and interpreted by Doctor Daniel Amen. Our effective treatment was focused on shifting bacteria, addressing coagulation, and reducing inflammation.

For more information on metabolites, see this post.

Magnetic Resonance Imaging

• Chronic fatigue and headache in post-COVID-19 syndrome: a radiological and clinical evaluation [2025]
  “T2-hyperintense lesions are common in patients with post-COVID-19 syndrome,”
• Hippocampal subfield volume alterations and associations with severity measures in long COVID and ME/CFS: A 7T MRI study [2025]
  “we found significant associations between hippocampal subfield volumes and severity measures of ‘Pain’, ‘Duration of illness’, ‘Severity of fatigue’, ‘Impaired concentration’, ‘Unrefreshing sleep’, and ‘Physical function’ in both conditions. These findings suggest that hippocampal alterations may contribute to the neurocognitive impairment experienced by long COVID and ME/CFS patients.”
• Hypothalamus Connectivity in Adolescent Myalgic Encephalomyelitis/Chronic Fatigue Syndrome [2024]
  “We observed weak-to-moderate evidence of increased degree, but not strength, of connections from the bilateral anterior-inferior (left: pd [%] = 99.18, median [95% CI] = -22.68[-40.96 to 4.45]; right: pd [%] = 99.86, median [95% CI] = -23.35[-38.47 to 8.20]), left anterior-superior (pd [%] = 99.33, median [95% CI] = -18.83[-33.45 to 4.07]) and total left hypothalamus (pd [%] = 99.44, median [95% CI] = -47.18[-83.74 to 11.03]) in the ME/CFS group compared with controls. Conversely, bilateral posterior hypothalamus degree decreased with increasing ME/CFS illness duration (left: pd [%] = 98.13, median [95% CI]: -0.47[-0.89 to 0.03]; right: pd [%] = 98.50, median [95% CI]:-0.43[-0.82 to 0.05]). Finally, a weak relationship between right intermediate hypothalamus connectivity strength and fatigue severity was identified in the ME/CFS group (pd [%] = 99.35, median [95% CI] = -0.28[-0.51 to 0.06]), which was absent in controls. These findings suggest changes in hypothalamus connectivity may occur in adolescents with ME/CFS, warranting further investigation.”
• Six-Week Supplementation with Creatine in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS): A Magnetic Resonance Spectroscopy Feasibility Study at 3 Tesla [2024]
  “Creatine supplementation over six weeks in ME/CFS patients increased brain creatine and improved fatigue and some aspects of cognition. “
• A Multimodal Magnetic Resonance Imaging Study on Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Feasibility and Clinical Correlation [2024]
  “The study demonstrates the feasibility of combining MRS and fMRI to capture neurochemical and neurophysiological features of ME/CFS in female participants.”
• Imbalanced Brain Neurochemicals in Long COVID and ME/CFS: A Preliminary Study Using MRI [2025]
  “Our study identified significantly elevated glutamate and N-acetyl-aspartate levels in long COVID and ME/CFS patients compared with healthy controls. “
• Blood-brain barrier disruption and sustained systemic inflammation in individuals with long COVID-associated cognitive impairment [2024]
• Cluster analysis to identify long COVID phenotypes using ¹²⁹Xe magnetic resonance imaging: a multicentre evaluation [2024]
  “We identified four ¹²⁹Xe MRI long COVID phenotypes with distinct characteristics. ¹²⁹Xe MRI can dissect pathophysiological heterogeneity of long COVID to enable personalised patient care.”
• Brain-regional characteristics and neuroinflammation in ME/CFS patients from neuroimaging: A systematic review and meta-analysis [2023]
  “These abnormalities, occurring in pivotal network hubs bridging reason and emotion, disrupt connections with the limbic system, contributing to the hallmark symptoms of ME/CFS. Furthermore, we discuss the regions where neuroinflammatory features are frequently observed and address critical neuroimaging limitations, including issues related to inter-rater reliability. This systematic review serves as a valuable guide for defining regions of interest (ROI) in future neuroimaging investigations of ME/CFS.”
• A prospective randomized, double-blind placebo-controlled study to evaluate the effectiveness of neuroprotective therapy using functional brain MRI in patients with post-covid chronic fatigue syndrome [2023]
  “ the study demonstrates the potential effectiveness of CCSA therapy in relation to a wide range of symptoms (chronic fatigue syndrome/ asthenic syndrome and cognitive impairment) in patients with post-COVID syndrome. The first time demonstrated the effectiveness of neuroprotective therapy after post-COVID asthenic syndrome with the use of high-tech neuroimaging techniques.”
• Hypothalamus volumes in adolescent Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS): impact of self-reported fatigue and illness duration [2023]
  “preliminary evidence was identified to suggest greater fatigue severity and longer illness duration were associated with greater right anterior-superior and superior-tubular volumes, respectively. “
• What lies beneath: White matter microstructure in pediatric myalgic encephalomyelitis/chronic fatigue syndrome using diffusion MRI [2023]
  “our findings suggest that white matter abnormalities may not be predominant in pediatric ME/CFS in the early stages following diagnosis. The discrepancy between our null findings and white matter abnormalities identified in the adult ME/CFS literature could suggest that older age and/or longer illness duration influence changes in brain structure and brain-behavior relationships that are not yet established in adolescence.”
• Brainstem volume changes in myalgic encephalomyelitis/chronic fatigue syndrome and long COVID patients [2023]
  ” Our study demonstrated an abnormal brainstem volume in both ME/CFS and long COVID consistent with the overlapping symptoms.”
• Connectivity Between Salience and Default Mode Networks and Subcortical Nodes Distinguishes Between Two Classes of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome [2023]
  “This work reports the remarkable finding that functional magnetic resonance imaging connectivity can differentiate between these two classes of ME/CFS.”
• Multimodal MRI of myalgic encephalomyelitis/chronic fatigue syndrome: A cross-sectional neuroimaging study toward its neuropathophysiology and diagnosis [2022] Study Proposal
• Volumetric differences in hippocampal subfields and associations with clinical measures in myalgic encephalomyelitis/chronic fatigue syndrome [2022]
  “we detected positive correlations between fatigue and hippocampus subfield volumes and a negative correlation between sleep disturbance score and the right CA1 body volume. In ME/CFS_ICC patients, we detected a strong negative relationship between fatigue and left hippocampus tail volume. Strong negative relationships were also detected between pain and SF36 physical scores and two hippocampal subfield volumes (left: GC-ML-DG head and CA4 head). Our study demonstrated that volumetric differences in hippocampal subfields have strong statistical inference for patients meeting the ME/CFS_ICC case definition and confirms hippocampal involvement in the cognitive and memory problems of ME/CFS_ICC patients.”
• Limbic Perfusion Is Reduced in Patients with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) [2021]
  ” the patient group showed significant hypoperfusion (uncorrected voxel wise p ≤ 0.001, FWE p ≤ 0.01) in several brain regions of the limbic system, including the anterior cingulate cortex, putamen, pallidum, and anterior ventral insular area. For the ME/CFS patients, the overall symptom severity score at rest was significantly associated with a reduced rCBF in the anterior cingulate cortex. The results of this study show that brain blood flow abnormalities in the limbic system may contribute to ME/CFS pathogenesis.”
• Neurochemical abnormalities in chronic fatigue syndrome: a pilot magnetic resonance spectroscopy study at 7 Tesla [2021]
  “The changes in glutathione and creatine are consistent with the presence of oxidative and energetic stress in CFS patients and are potentially remediable by nutritional intervention. “
• Subcortical brain segment volumes in Gulf War Illness and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome [2021]
  “CFS and GWI were appropriate “illness controls” for each other. The different patterns of adjusted segment volumes suggested that sexual dimorphisms contributed to pathological changes. Previous volumetric studies may need to be reevaluated to account for gender differences.”
• High Prevalence of Perineural Cysts in Patients with Fibromyalgia and Chronic Fatigue Syndrome [2021]
  “ In patients diagnosed with FM or CFS, the prevalence of TCs was three times higher than that in the general population”
• Neuroimaging characteristics of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS): a systematic review [2020]
  “ Neuroimaging studies of ME/CFS have frequently observed additional brain area recruitment during cognitive tasks and abnormalities in the brain stem. The frequent observation of additional brain area recruitment and consistent observation of sluggish fMRI signal response suggest abnormal neurovascular coupling in ME/CFS.”
• Using structural and functional MRI as a neuroimaging technique to investigate chronic fatigue syndrome/myalgic encephalopathy: a systematic review [2020]
  “The most consistent finding was CFS/ME exhibited increased activations and recruited additional brain regions. Tasks with increasing load or complexity produced decreased activation in task-specific brain regions.”
• Mapping of pathological change in chronic fatigue syndrome using the ratio of T1- and T2-weighted MRI scans [2020]
  “he voxel-based group comparison with sub-millimetre resolution voxels detected very significant clusters with increased T1w/T2w in ME/CFS, mostly in subcortical grey matter, but also in brainstem and projection WM tracts. “
• Neuroimaging markers of chronic fatigue in older people: a narrative review [2021]
  “Fatigue was associated with reduced hippocampus volumes and with hippocampal amyloid deposition. Regarding the association between fatigue and the circuit of basal ganglia, putamen and thalamus were associated with physical fatigability, whereas amygdala and thalamus with mental fatigability”
• Machine Learning Detects Pattern of Differences in Functional Magnetic Resonance Imaging (fMRI) Data between Chronic Fatigue Syndrome (CFS) and Gulf War Illness (GWI) [2020]
  “We concluded that CFS and GWI are significantly differentiable using a pattern of fMRI activity based on an ensemble machine learning model.”
• Altered Structural Brain Networks Related to Adrenergic/Muscarinic Receptor Autoantibodies in Chronic Fatigue Syndrome [2020]
  “Our findings suggest that β1 AdR-Ab and β2 AdR-Ab are potential markers of ME/CFS.”
• A systematic review of neurological impairments in myalgic encephalomyelitis/ chronic fatigue syndrome using neuroimaging techniques [2020]
  “Changes in gray and white matter volumes, cerebral blood flow, brain structure, sleep, EEG activity, functional connectivity and cognitive function.”
• A Machine Learning Approach to the Differentiation of Functional Magnetic Resonance Imaging Data of Chronic Fatigue Syndrome (CFS) From a Sedentary Control [2020]
  “This study was able to uncover a pattern of activated neurological regions that differentiated CFS from Control. “
• Intra brainstem connectivity is impaired in chronic fatigue syndrome [2019]
  “In ME/CFS, connections were absent between medulla and midbrain nuclei, although hippocampal connections with these nuclei were enhanced. When corresponding correlations from HC and ME/CFS were compared, ME/CFS connectivity deficits were detected within the brainstem between the medulla and cuneiform nucleus and between the brainstem and hippocampus and intralaminar thalamus, but only during task. “
• Evidence of widespread metabolite abnormalities in Myalgic encephalomyelitis/chronic fatigue syndrome: assessment with whole-brain magnetic resonance spectroscopy [2020]
• Severe posterior hypometabolism but normal perfusion in a patient with chronic fatigue syndrome/myalgic encephalomyelitis revealed by PET/MRI [2019]
• Hyperintense sensorimotor T1 spin echo MRI is associated with brainstem abnormality in chronic fatigue syndrome [2018]
  “the T1wSE group comparison detected decreased signal-levels in CFS in a brainstem region (cluster-based inference controlled for family wise error rate, P_FWE= 0.002), and increased signal-levels in large bilateral clusters in sensorimotor cortex white matter (cluster P_FWE < 0.0001). Moreover, the brainstem T1wSE values were negatively correlated with the sensorimotor values for both CFS (R² = 0.31, P = 0.00007) and healthy controls (R² = 0.34, P = 0.0009), “
• Brain abnormalities in myalgic encephalomyelitis/chronic fatigue syndrome: Evaluation by diffusional kurtosis imaging and neurite orientation dispersion and density imaging [2019]
  “In the ME/CFS patients, we observed significant FA decreases in the genu of the corpus callosum and the anterior limb of the right internal capsule (P < 0.05),”
• Brain function characteristics of chronic fatigue syndrome: A task fMRI study [2018]
  “The results suggest the brain responds differently to a cognitive challenge in patients with CFS, with recruitment of wider regions to compensate for lower information capacity.”
• Intracranial compliance is associated with symptoms of orthostatic intolerance in chronic fatigue syndrome [2018]
  “Within the patient group, higher severity of OI symptoms were associated with lower intracranial compliance (r = -.346, p = .033) and higher resting perfusion (r = .337, p = .038). In both groups intracranial compliance was negatively correlated with cerebral perfusion.”
• Structural brain changes versus self-report: machine-learning classification of chronic fatigue syndrome patients [2018]
  “The sMRI model achieved 79.58% classification accuracy. The SR (accuracy = 95.95%) outperformed both sMRI models. Estimates from multiple brain areas related to cognition, emotion, and memory contributed strongly to group classification. “
• Cortical hypoactivation during resting EEG suggests central nervous system pathology in patients with chronic fatigue syndrome [2018]
  “The implications of abnormal cortical sources in patients with CFS are discussed.”
• Elevations of Ventricular Lactate Levels Occur in Both Chronic Fatigue Syndrome and Fibromyalgia [2017]
  “Mean CSF lactate levels in CFS, FM and CFS+FM did not differ among the three groups, but were all significantly higher than the mean values for control subjects.”
• Decreased Connectivity and Increased Blood Oxygenation Level Dependent Complexity in the Default Mode Network in Individuals with Chronic Fatigue Syndrome [2018]
  “This study showed that DMN activity is more complex and less coordinated in CFS, suggesting brain network analysis could be potentially used as a diagnostic biomarker for CFS.”
• Grey and white matter differences in Chronic Fatigue Syndrome – A voxel-based morphometry study [2017]
  “patients had larger GM volume and lower WM volume. The voxel-wise analysis showed increased GM volume in several structures including the amygdala and insula in the patient group. Reductions in WM volume in the patient group were seen primarily in the midbrain, pons and right temporal lobe.”
• Altered right anterior insular connectivity and loss of associated functions in adolescent chronic fatigue syndrome [2017]
  “These results suggest a distinct biological signature of adolescent CFS and might represent a fundamental role of the dAI in motivated behavior.”
• Multimodal and simultaneous assessments of brain and spinal fluid abnormalities in chronic fatigue syndrome and the effects of psychiatric comorbidity [2017]
  “Thirteen of 26 patients had abnormal values on two or more of these 4 brain-related variables.”
• Prefrontal Structure Varies as a Function of Pain Symptoms in Chronic Fatigue Syndrome [2017]
  “Individual variation in the presence of pain, rather than fatigue, is associated with neuronal alterations in the dorsolateral prefrontal cortex of patients with CFS.”
• Chronic fatigue syndrome in women assessed with combined cardiac magnetic resonance imaging [2016]
  “In patients with CFS, CMR demonstrated lower LV dimensions and a mildly reduced LV function. The presence of myocardial fibrosis in some CFS patients suggests that CMR assessment of cardiac involvement is warranted as part of the scientific exploration, which may imply serial non-invasive examinations.”
• Autonomic correlations with MRI are abnormal in the brainstem vasomotor centre in Chronic Fatigue Syndrome [2016]
  “Abnormal regressions were detected in nuclei of the brainstem vasomotor centre, midbrain reticular formation and hypothalamus, but also in limbic nuclei involved in stress responses and in prefrontal white matter.”
• Evidence in chronic fatigue syndrome for severity-dependent upregulation of prefrontal myelination that is independent of anxiety and depression [2015]
  “MRI regressions with depression itself only detected associations with WM volume, also located in prefrontal WM”
  
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Bottom Line Model

I believe the best model is based on Evidence of widespread metabolite abnormalities in Myalgic encephalomyelitis/chronic fatigue syndrome: assessment with whole-brain magnetic resonance spectroscopy [2020]. Metabolite abnormalities can be a direct result of microbiome dysfunctions. Those abnormalities are very treatable using microbiome tests and expert systems such as generated by Microbiome Prescription.