Literature REVIEW

Neurost ruct ural Dif ferences in Schizophrenia as Seen on MRI: A Br ief

Lit erat ure Review

The first magnetic resonance image (MRI) of a human?s internal anatomy

was taken in 1977 (Tretkoff, 2006). From there, the technology continued to

evolve and in 1989, with MRI machines safe and commercially available, then

President George Bush launched a national initiative to leverage MRI to explore

the great unknown ? no, not space, but the human brain. The National Institute

of Health (NIH) generously distributed grants to any research group seeking to

study the brain (Goldstein, 1994). One of the specialties that benefited the most

from Bush?s ?Decade of the Brain? was schizophrenia research.

Schizophrenia is a severe and pervasive mental illness and chronic brain

disorder diagnosed in 1 out of every 222 people worldwide (WHO, 2022). It is

characterized by persistent hallucinations (most commonly auditory), delusional

thinking, and a host of ?negative? symptoms, like apathy, anhedonia, thought

disorder, and flat affect (or, put another way, symptoms that represent the

absence of features associated with healthy psychological functioning).

Schizophrenia leads to significant functional impairment, reduced quality of life,

and decreased ability to live independently. It strikes right when young adults

are primed to strike out on their own, eager to fulfill their dreams. For these

reasons, schizophrenia is considered one of the most devastating mental

illnesses, and prior to the 1990s we understood very litt le about it.

MRI offered the field of psychiatry an opportunity to answer long-held

questions and explain consistent observations clinicians had been making for

years. The purpose of the following brief review is to highlight some of key

MRI-related findings regarding brain structure in schizophrenia, how we are

currently using that information, and critically evaluate what these structural

differences may (or may not) represent.

The introduction might include new sources/in-text citations (references you did not review in your annotated bibliography). These serve a different purpose. These support individual claims you make (like statistics, storytelling, etc.). You aren't reviewing them in detail as you are doing with the "main" three.

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The introduction answers certain questions. Like, "What is the research topic?" Here, the answer is "schizophrenia research using MRI."

Why is the topic important?

It is one of the most devastating mental illnesses. Claim is justified with some compelling evidence.

What is the scope/objective?

The scope is "brief " as it 's put, and the student 's objective is three-fold:1) lay out the MRI-findings, 2) discuss how those findings are currently being used, and 3) to do so with a critical eye. This objective statement also segues into the body of the paper.

A Br ief Overview of Brain Dif ferences in Schizophrenia

In his seminal 2005 literature review, Buckley explored the structural

abnormalities consistently found in schizophrenia patients on MRI. The

preponderance of the evidence to that point found patient samples to have

significantly enlarged ventricles, substantive loss of cortical tissue, and smaller

temporal lobes compared to healthy controls. Prefrontal cortex differences

were also consistently observed, which explains the significant difficulties

patients have with executive functions, decision-making, and social

engagement. Gender and age differences were also noted, with young males

showing more structural changes than other demographic groups ? consistent

with clinical observations that young men seem to struggle with the condition

more than any other population. These findings derived from a wide variety of

patient cohorts and revealed a general pattern of tissue loss. Researchers

hypothesized that these structural changes result from neurodevelopmental

causes ? genetics, prenatal exposure to infection, malnutrition, or distress, and

inflammation caused by overactive immune systems. The course of the illness

itself (i.e., the brain responding to antipsychotic medication, or the brain

responding to behavioral habits associated with the disorder) is another

explanation for the differences.

Buckley?s review is indeed comprehensive, citing over 111 articles, and

offers a robust look at structural abnormalities noted to that point. However,

the author makes sure to point out several limitations that plague researchers

who study schizophrenia with MRI. First, structural MRI studies are a

?mammoth undertaking,? with several nuanced technical issues that render

generalization difficult. Second, no two patients are the same, so when

scientists compare someone with chronic schizophrenia to someone who just

onset, for example, then some amount of error variance is injected. Third, these

Notice that a lot of this verbiage comes from the original annotated bibliography (Unit 3's example paper). This is okay to do. But, also notice that the student expanded upon their original work, fleshing it out with a litt le more information and transitions so that this reads more like an essay than "just" a summary.

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Discussion of the limitations of this study. You'll do this in every section, as you did in the annotated bibliography.

studies are best done longitudinally and because they require large sample

sizes with a difficult-to-nail-down population, attrit ion is an issue. This suggests

that even consistent findings should be interpreted with caution and serves as

a reminder that large datasets speak to generalities and not the individual.

This article provides a broad look into how MRI is used to discover and

understand structural differences in schizophrenia and serves as an

informative starting point for understanding how the brains of those with

schizophrenia differ from those who do not. Buckley also lists the issues with

this sort of research, some of which have been overcome since 2005. For

example, scientists have now standardized MRI protocols across disciplines so

that comparisons are more accurate (Sharma & Saindane, 2020).

Given that we now know what a brain with schizophrenia looks like on

imaging, how can we use this information to help those coping with the

disorder? Here, we turn to researchers who are leveraging these biomarkers to

aid in diagnosis and early detection. Important work since we do know that

early intervention is imperative; the longer the one is untreated, the more

difficult the disorder is to treat (Cheng & Schepp, 2016).

Diagnosing Schizophrenia ? An MRI-Based Algor it hm

When medical doctors want to diagnose cancer, they order imaging and

bloodwork. When they want to diagnose infection, they take a sample, grow it

in a petri dish, and look at it under a microscope. A psychiatrist, however, does

not have the luxury of traditional bioindicators to help them diagnose a

condition. There is no blood test for depression, and you cannot see PTSD

under a microscope. Mental health practitioners rely on behavioral

observation, interview, history taking, and psych testing. These methods have

served the field well, but there is still a want for biomarkers to help diagnose,

as early as possible, since waiting for symptoms to become severe and

impairing enough to affect behavior is often too late.

Now that we have decades of consistent MRI findings under our belts,

Example of expanding upon the take home messages and the "tie in" to the thesis. In this instance, another source was brought into justify a relevant claim.

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This paragraph is an example of how to segue from one body section to another, making a smooth, sensible transition for the reader. It includes another scholarly source since the student wanted to end with the point that early detection is important. This claim needed to be backed up, so they used this Cheng & Schepp article to do so.

APA formatted subject headings.

This is an original paragraph (not included in annotated bib). It was added to transition, explain, and add meat to the argument being made. It also compels the reader.

we are closer than ever to having reliable diagnostic biomarkers for

schizophrenia. In a 2020 study, Oh et al. tested the hypothesis that schizophrenia

can be diagnosed earlier in patients using a deep learning algorithm, a computer

process that identifies specific changes in brain structure on MRI that represent

classic schizophrenia patterns. The authors were able to successfully identify

patients with schizophrenia based on these structural brain changes (particularly

right temporal and right parietal areas). The algorithm not only identified patients

earlier, but more correctly. Of note, a sample of human clinicians did not

accurately discriminate schizophrenia patients when given MR images.

The algorithm was trained on a robust dataset (over 800 studies across 18

years) and its accuracy was impressive, but there were several limitations to this

study. The algorithm was based on binary classification (schizophrenia/no

schizophrenia) which does not necessarily apply to the real world of comorbid

diagnoses. That, and it only provides a binary output and not a nuanced look into

symptom profiles of individual patients. In other words, all it can do is determine

whether someone has schizophrenia or not, not whether they experience hostile

or friendly auditory hallucinations, whether they present with more negative vs.

positive symptoms, or what their current risk of suicide is ? things that matter

most for treatment decisions. And while human clinicians did not identify

schizophrenia as well as the algorithm, it is not a fair comparison ? clinicians do

not traditionally use MRI to diagnose. Before now, diagnosing by MRI has not

really been an option or supported by the research.

Oh et al.?s (2020) study shows that structural brain changes that

characterize schizophrenia can be an effective diagnostic biomarker of the

disease. This gives momentum to a trend we are currently seeing in broader

psychiatric research (Razafsha et al., 2015), and instills hope regarding early

identification and prevention. As outlined above, there are limitations to relying

on biomarkers, but the addition of them to the arsenal of diagnostic processes

Sections taken almost verbatim from annotated bibliography.

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This explanation was added, though, to flesh out the idea that binary classifications are not super helpful.

New paragraph aimed at helping put this study in a larger context and, with the paragraph after it, transition to the next section. New reference (Razafsha et al.) needed to

support claim that biomarker research is trending.

we are already using strengthens a clinician?s ability to help those in need.

But what about treatment? Do biomarkers imply anything about what we

can do to help? What if biological markers don?t represent the cause of the

disorder, but rather the consequence? Or maybe structural brain changes are

really the result of another risk factor?

St ruct ural Change in Schizophrenia ? Cause or Consequence

MRI data have shown us that, indeed, schizophrenia does look a certain

way on MRI. And we can use algorithms to help us identify those trends for

diagnostic purposes. But what MRIs cannot tell us is why those structural

abnormalities exist. It does not tell us whether these abnormalities are what is

causing schizophrenia, or if they are simply the result of it.

Haddad et al. (2015) give us a clear example of this chicken-or-the-egg

scenario by focusing on another commonality that many people with

schizophrenia share - this one sociodemographic and not biological in nature.

Urban upbringing is a well-known environmental risk factor for schizophrenia,

especially in males. But the question has always been? why? What is the

mechanism? These authors turned to MRI data to see if reduced gray matter in

the perigenual anterior cingulate cortex (pACC), the dorsolateral prefrontal

cortex (DLPFC), and hippocampus, might be the answer. These three areas are

particularly vulnerable to chronic stress exposure (hypothesized to be the

instigating factor in urban living) and have also been shown to have reduced

volume in schizophrenia patients.

To avoid confounding variables, the authors chose to study healthy

controls. If urban upbringing is associated with changes in the DLPFC, pACC, and

hippocampus and reduced gray matter (areas of change consistently noted in

schizophrenia, too) then this would support the hypothesis. Results yielded an

inverse relationship between early life urbanicity and gray matter volume in the

Subject heading is reflective of the topic, and not the tit le of the article that is reviewed.

These two paragraphs come nearly verbatim from the annotated bibliography. Just a nod to the fact that you already have a good chunk of this paper already written in the form of your Unit 3 annotations!

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Use "et al." as shown here for sources that have more than two authors.

DLPFC. In males with higher early-life urban exposure, the volume of the pACC

was reduced. However, no significant results were found for the hippocampus.

Thus, their hypothesis was partially supported.

One limitation of this study was the very basic definition of ?urbanicity?

(population size), which may not be the most robust indicator ?urban life?. Second,

we assume urbanicity means more stress, but this study did not measure stress

specifically. Third, the participants were healthy controls; the implications for

schizophrenia are just that ? implied.

When structural changes are noted in schizophrenia, the assumption is

that these changes are somehow causal and diagnostic in and of themselves. This

article provides an important contrast to this assumption ? structural changes

may not be causal, but simply be the result of another more central risk factor ?

like a sociocultural phenomenon. This is a cautionary tale about inferring

causation from correlation. Perhaps it?s not the brain that needs changed or

studied under a microscope, but the early context in which the brain develops.

Conclusion

Without a doubt, the evolution of MRI technology has advanced our

understanding of schizophrenia. "Decade of the Brain" research gave us a peek

behind the curtain of skin and bone, allowing us to see what schizophrenia looks

like and/or what the disease process does to the human brain. Buckley's (2005)

comprehensive literature review consolidated the decade?s findings. It is a truism

that, on average, people with schizophrenia have enlarged ventricles, lower

amounts of of cortical tissue, smaller temporal lobes, and reduced volume and

connectivity in the prefrontal cortex compared to those without schizophrenia.

Researchers have taken this wealth of information, applied computer

science principles, and are now using MRI data to diagnose schizophrenia earlier

and more efficiently than human clinicians. Schizophrenia research leads the

Remember, your third article is the one that presents a counterpoint or challenge to the points you have been making throughout. You've already done this in your annotated bibliography, but pointing it out again since it is required in this assignment as well.

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A transitional paragraph added to wrap up this section and hammer home the argument being made.

The conclusion answers several questions. For example, "What are the main findings?" Here, the main findings are "we know what a brain with schizophrenia looks like on MRI and we are using that information to help. "

charge here, as studies seeking to establish biomarkers for a variety of mental

health conditions are now popular and plentiful (Carvalho et al., 2020).

But a careful read of the literature does issue some cautionary tales.

While structural brain differences in schizophrenia are, in fact, there, we still do

not know exactly why or what it means. Are those differences to blame for

symptoms and impairment? Or are they simply the result of it? Or, as Haddad et

al. (2015), point out, do those structural brain differences simply reflect a more

powerful cause that we should turn our focus toward instead, like the

sociodemographic situation at-risk individuals are raised in?

The field of mental health is tasked with figuring out how to combine tried

and true clinician diagnosis, expertise, and psychological testing tools with

biological diagnostic indicators to offer their patients early diagnosis as well as

early and scientifically supported treatment. Continuing along this path ? keeping

both oars in the water to so speak (biological research along with continued

attention to social science studies) is the solution and researchers are well on

their way toward achieving that goal; hopeful news indeed for the twenty million

people who suffer from schizophrenia worldwide.

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What limitations/challenges are noted in the research??

This student reminds the reader that we don't know what causes these structural differences or how to best leverage them yet.

What are future directions/areas for further study?

Here, the student mentions that its about combining the new with the old to have 'both oars in the water " - biomarkers plus tried and true clinical assessment techniques.

What is the significance of this topic?

A reminder that schizophrenia is a widespread, devastating illness and the two-oared approach is the best way to meet their needs.

References

Buckley, P. (2005). Neuroimaging of schizophrenia: Structural abnormalities and

pathophysiological implications. Neuropsychiatric Disease and Treatment, 1(3),

193-204. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2416751/

Carvalho, A., Solmi, M., Sanches, M., Machado, M., Stubbs, B., Ajnakina, O., Sherman,

C., Sun, Y., Liu, C., Brunoni, A., Pigato, G., Fernandes, B., Bortolato, B., Husain,

M., Dragioti, E., Firth, J., Cosco, T., Maes, M., Berk, M., Lanctôt, K., Vieta, E.,

Pizzagalli, D., Smith, L., Fusar-Poli, P.,Kurdyak,P.,Fornaro,M.,Rehm, J.,

&Herrmann, N. (2020). Evidence-based umbrella review of 162 peripheral

biomarkers for major mental disorders. Translational Psychiatry, 10(152).

https://doi.org/10.1038/s41398-020-0835-5

Goldstein, M. (1994). Decade of the brain: An agenda for the nineties.Western

Journal of Medicine, 161(3), 239?241.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1011403/

Haddad, L., Schäfer, A., Streit, F., Lederbogen, F., Grimm, O., Wüst, S., Deuschle, M.,

Kirsch,P., Tost, H., & Meyer-Lindenberg, A. (2015). Brain structure correlates

of urban upbringing, an environmental risk factor for schizophrenia.

Schizophrenia Bulletin, 41(1), 115?122. https://doi.org/10.1093/schbul/sbu072

Oh, J., Oh, B.-L., Lee, K.-U., Chae, J.-H., & Yun, K. (2020). Identifying schizophrenia

using structural MRI with a deep learning algorithm. Frontiers in Psychiatry,

11. https://www.doi.org/10.3389/fpsyt.2020.00016

Razafsha, M., Khaku, A., Azari, H., Alawieh, A., Behforuzi, H., Fadlallah, B., Kobeissy,

F., Wang, K., & Gold, M. (2015). Biomarker identification in psychiatric

disorders: From neuroscience to clinical practice. Journal of Psychiatric

Practice, 21(1), 37-48.

https://www.doi.org/10.1097/01.pra.0000460620.87557.02

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Remember to cite your sources in APA format You will at least have five (more is okay). Three from the main articles you are reviewing (from your annotated bibliography) and at least two additional references to support other claims you make throughout your paper.

Sharma, P. S., & Saindane, A. M. (2020). Standardizing magnetic

resonance imaging protocols across a large radiology enterprise: Barriers

and solutions. Current Problems in Diagnostic Radiology, 49(5), 312?316.

https://doi.org/10.1067/j.cpradiol.2020.01.012

Tretkoff, E. (2006). This month in physics history: July, 1977: MRI uses fundamental

physics for clinical diagnosis. APS News, 15(7).

https://www.aps.org/publications/apsnews/200607/history.cfm

World Health Organization (WHO) (2022). Schizophrenia.

https://www.who.int/news-room/fact-sheets/detail/schizophrenia

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Notice that nearly all sources come from academic, peer-reviewed journal articles. The WHO website is only included to cite a statistic used.